Skip to main content

Thank you for visiting nature.com. 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.

  • Review Article
  • Published:

Identification of clinically relevant biomarkers of epileptogenesis — a strategic roadmap

Subjects

Abstract

Onset of many forms of epilepsy occurs after an initial epileptogenic insult or as a result of an identified genetic defect. Given that the precipitating insult is known, these epilepsies are, in principle, amenable to secondary prevention. However, development of preventive treatments is difficult because only a subset of individuals will develop epilepsy and we cannot currently predict which individuals are at the highest risk. Biomarkers that enable identification of these individuals would facilitate clinical trials of potential anti-epileptogenic treatments, but no such prognostic biomarkers currently exist. Several putative molecular, imaging, electroencephalographic and behavioural biomarkers of epileptogenesis have been identified, but clinical translation has been hampered by fragmented and poorly coordinated efforts, issues with inter-model reproducibility, study design and statistical approaches, and difficulties with validation in patients. These challenges demand a strategic roadmap to facilitate the identification, characterization and clinical validation of biomarkers for epileptogenesis. In this Review, we summarize the state of the art with respect to biomarker research in epileptogenesis and propose a five-phase roadmap, adapted from those developed for cancer and Alzheimer disease, that provides a conceptual structure for biomarker research.

Key points

  • Many forms of epilepsy manifest and/or are diagnosed months or even years after an epileptogenic insult or identification of a genetic defect.

  • Epilepsies that result from an insult or genetic defect could be amenable to secondary prevention but preventive treatments are not available.

  • Development of preventive therapies is complicated by the fact that only a subset of at-risk individuals develop clinical epilepsy.

  • Biomarkers are needed to clearly identify individuals who have the highest risk of developing epilepsy after an epileptogenic insult.

  • We propose a strategic roadmap designed to facilitate the identification, characterization and clinical validation of biomarkers for epileptogenesis.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: SWOT analysis for the biomarker development roadmap.

Similar content being viewed by others

References

  1. World Health Organization. Epilepsy. Epilepsy: Key Facts. https://www.who.int/news-room/fact-sheets/detail/epilepsy (2019).

  2. Pitkanen, A. & Lukasiuk, K. Mechanisms of epileptogenesis and potential treatment targets. Lancet Neurol. 10, 173–186 (2011).

    Article  PubMed  Google Scholar 

  3. GBD 2016 Traumatic Brain Injury and Spinal Cord Injury Collaborators. Global, regional, and national burden of traumatic brain injury and spinal cord injury, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 18, 56–87 (2019).

    Article  Google Scholar 

  4. Simonato, M. et al. The challenge and promise of anti-epileptic therapy development in animal models. Lancet Neurol. 13, 949–960 (2014). This review highlights the need for biomarker identification to develop disease-modifying therapies of epilepsy.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Loscher, W. & Schmidt, D. Modern antiepileptic drug development has failed to deliver: ways out of the current dilemma. Epilepsia 52, 657–678 (2011). This study shows that newer anti-epileptic drugs have provided limited improvements over the older ones.

    Article  PubMed  Google Scholar 

  6. Simonato, M. et al. Finding a better drug for epilepsy: preclinical screening strategies and experimental trial design. Epilepsia 53, 1860–1867 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Pitkanen, A. et al. Advances in the development of biomarkers for epilepsy. Lancet Neurol. 15, 843–856 (2016).

    Article  CAS  PubMed  Google Scholar 

  8. Pitkanen, A., Ekolle Ndode-Ekane, X., Lapinlampi, N. & Puhakka, N. Epilepsy biomarkers - toward etiology and pathology specificity. Neurobiol. Dis. 123, 42–58 (2019). This review presents a summary of potential biomarkers of epilepsy.

    Article  CAS  PubMed  Google Scholar 

  9. FDA-NIH Biomarker Working Group. BEST (Biomarkers, EndpointS, and Other Tools) Recommendations https://www.ncbi.nlm.nih.gov/books/NBK326791/ (2016). A reference resource for the definition and classification of biomarkers.

  10. Meguid, N. et al. Altered S100 calcium-binding protein B and matrix metallopeptidase 9 as biomarkers of mesial temporal lobe epilepsy with hippocampus sclerosis. J. Mol. Neurosci. 66, 482–491 (2018).

    Article  CAS  PubMed  Google Scholar 

  11. Asadollahi, M. & Simani, L. The diagnostic value of serum UCHL-1 and S100-B levels in differentiate epileptic seizures from psychogenic attacks. Brain Res. 1704, 11–15 (2018).

    Article  PubMed  Google Scholar 

  12. Simani, L., Elmi, M. & Asadollahi, M. Serum GFAP level: a novel adjunctive diagnostic test in differentiate epileptic seizures from psychogenic attacks. Seizure 61, 41–44 (2018).

    Article  PubMed  Google Scholar 

  13. Huang, Q., Liu, J., Shi, Z. & Zhu, X. Correlation of MMP-9 and HMGB1 expression with the cognitive function in patients with epilepsy and factors affecting the prognosis. Cell Mol. Biol. 66, 39–47 (2020).

    Article  PubMed  Google Scholar 

  14. Nass, R. D., Wagner, M., Surges, R. & Holdenrieder, S. Time courses of HMGB1 and other inflammatory markers after generalized convulsive seizures. Epilepsy Res. 162, 106301 (2020).

    Article  CAS  PubMed  Google Scholar 

  15. Pauletti, A. et al. Targeting oxidative stress improves disease outcomes in a rat model of acquired epilepsy. Brain 142, e39 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Alles, J. et al. An estimate of the total number of true human miRNAs. Nucleic Acids Res. 47, 3353–3364 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Toffolo, K. et al. Circulating microRNAs as biomarkers in traumatic brain injury. Neuropharmacology 145, 199–208 (2018).

    Article  PubMed  Google Scholar 

  18. Vijayan, M. & Reddy, P. H. Peripheral biomarkers of stroke: focus on circulatory microRNAs. Biochim. Biophys. Acta 1862, 1984–1993 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Dewdney, B. et al. Circulating microRNAs as biomarkers for acute ischemic stroke: a systematic review. J. Stroke Cerebrovasc. Dis. 27, 522–530 (2018).

    Article  PubMed  Google Scholar 

  20. Henshall, D. C. Manipulating microRNAs in murine models: targeting the multi-targeting in epilepsy. Epilepsy Curr. 17, 43–47 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Brennan, G. P. & Henshall, D. C. MicroRNAs as regulators of brain function and targets for treatment of epilepsy. Nat. Rev. Neurol. 16, 506–519 (2020).

    Article  CAS  PubMed  Google Scholar 

  22. Gorter, J. A. et al. Hippocampal subregion-specific microRNA expression during epileptogenesis in experimental temporal lobe epilepsy. Neurobiol. Dis. 62, 508–520 (2014).

    Article  CAS  PubMed  Google Scholar 

  23. Roncon, P. et al. MicroRNA profiles in hippocampal granule cells and plasma of rats with pilocarpine-induced epilepsy – comparison with human epileptic samples. Sci. Rep. 5, 14143 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Brennan, G. P. et al. Genome-wide microRNA profiling of plasma from three different animal models identifies biomarkers of temporal lobe epilepsy. Neurobiol. Dis. 144, 105048 (2020).

    Article  CAS  PubMed  Google Scholar 

  25. Raoof, R. et al. Dual-center, dual-platform microRNA profiling identifies potential plasma biomarkers of adult temporal lobe epilepsy. EBioMedicine 38, 127–141 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Kamnaksh, A. et al. Harmonization of pipeline for preclinical multicenter plasma protein and miRNA biomarker discovery in a rat model of post-traumatic epileptogenesis. Epilepsy Res. 149, 92–101 (2019).

    Article  CAS  PubMed  Google Scholar 

  27. Iwuchukwu, I. et al. MicroRNA regulatory network as biomarkers of late seizure in patients with spontaneous intracerebral hemorrhage. Mol. Neurobiol. 57, 2346–2357 (2020).

    Article  CAS  PubMed  Google Scholar 

  28. Enright, N., Simonato, M. & Henshall, D. C. Discovery and validation of blood microRNAs as molecular biomarkers of epilepsy: ways to close current knowledge gaps. Epilepsia Open 3, 427–436 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Trelinska, J. et al. Abnormal serum microRNA profiles in tuberous sclerosis are normalized during treatment with everolimus: possible clinical implications. Orphanet J. Rare Dis. 11, 129 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Wang, X. et al. MicroRNA-134 plasma levels before and after treatment with valproic acid for epilepsy patients. Oncotarget 8, 72748–72754 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Redell, J. B., Moore, A. N., Ward, N. H. 3rd, Hergenroeder, G. W. & Dash, P. K. Human traumatic brain injury alters plasma microRNA levels. J. Neurotrauma 27, 2147–2156 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Mitra, B. et al. Plasma micro-RNA biomarkers for diagnosis and prognosis after traumatic brain injury: a pilot study. J. Clin. Neurosci. 38, 37–42 (2017).

    Article  CAS  PubMed  Google Scholar 

  33. Hogg, M. C. et al. Elevation in plasma tRNA fragments precede seizures in human epilepsy. J. Clin. Invest. 129, 2946–2951 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  34. McArdle, H. et al. “TORNADO” - Theranostic One-Step RNA Detector; microfluidic disc for the direct detection of microRNA-134 in plasma and cerebrospinal fluid. Sci. Rep. 7, 1750 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Cheng, Y., Dong, L., Zhang, J., Zhao, Y. & Li, Z. Recent advances in microRNA detection. Analyst 143, 1758–1774 (2018).

    Article  CAS  PubMed  Google Scholar 

  36. Diamond, M. L. et al. IL-1β associations with posttraumatic epilepsy development: a genetics and biomarker cohort study. Epilepsia 55, 1109–1119 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Ding, K., Gupta, P. K. & Diaz-Arrastia, R. in Translational Research in Traumatic Brain Injury Ch. 14 (eds Laskowitz, D. & Grant, G.) (CRC Press/Taylor and Francis Group, 2016).

  38. Pitkanen, A. & Immonen, R. Epilepsy related to traumatic brain injury. Neurotherapeutics 11, 286–296 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Salazar, A. M. & Grafman, J. Post-traumatic epilepsy: clinical clues to pathogenesis and paths to prevention. Handb. Clin. Neurol. 128, 525–538 (2015).

    Article  PubMed  Google Scholar 

  40. Agoston, D. V. & Kamnaksh, A. Protein biomarkers of epileptogenicity after traumatic brain injury. Neurobiol. Dis. 123, 59–68 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Lewis, D. V. et al. Hippocampal sclerosis after febrile status epilepticus: the FEBSTAT study. Ann. Neurol. 75, 178–185 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Huttunen, J. K. et al. Detection of hyperexcitability by functional magnetic resonance imaging after experimental traumatic brain injury. J. Neurotrauma 35, 2708–2717 (2018).

    Article  PubMed  Google Scholar 

  43. Vezzani, A., Pascente, R. & Ravizza, T. Biomarkers of epileptogenesis: the focus on glia and cognitive dysfunctions. Neurochem. Res. 42, 2089–2098 (2017).

    Article  CAS  PubMed  Google Scholar 

  44. Nicolo, J. P., O’Brien, T. J. & Kwan, P. Role of cerebral glutamate in post-stroke epileptogenesis. Neuroimage Clin. 24, 102069 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Shultz, S. R. et al. Can structural or functional changes following traumatic brain injury in the rat predict epileptic outcome? Epilepsia 54, 1240–1250 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Cleeren, E. et al. Positron emission tomography imaging of cerebral glucose metabolism and type 1 cannabinoid receptor availability during temporal lobe epileptogenesis in the amygdala kindling model in rhesus monkeys. Epilepsia 59, 959–970 (2018).

    Article  CAS  PubMed  Google Scholar 

  47. Toczek, M. T. et al. PET imaging of 5-HT1A receptor binding in patients with temporal lobe epilepsy. Neurology 60, 749–756 (2003).

    Article  CAS  PubMed  Google Scholar 

  48. Guiard, B. P. & Di Giovanno, G. Central serotonin-2A (5-HT2A) receptor dysfunction in depression and epilepsy: the missing link? Front. Pharmacol. 6, 46 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Bascunana, P. et al. PET neuroimaging reveals serotonergic and metabolic dysfunctions in the hippocampal electrical kindling model of epileptogenesis. Neuroscience 409, 101–110 (2019).

    Article  CAS  PubMed  Google Scholar 

  50. Choi, H. et al. In vivo imaging of mGluR5 changes during epileptogenesis using [11C]ABP688 PET in pilocarpine-induced epilepsy rat model. PLoS ONE 9, e92765 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Celli, R. et al. Targeting metabotropic glutamate receptors in the treatment of epilepsy: rationale and current status. Expert Opin. Ther. Targets 23, 341–351 (2019).

    Article  CAS  PubMed  Google Scholar 

  52. Gershen, L. D. et al. Neuroinflammation in temporal lobe epilepsy measured using positron emission tomographic imaging of translocator protein. JAMA Neurol. 72, 882–888 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Dickstein, L. P. et al. Neuroinflammation in neocortical epilepsy measured by PET imaging of translocator protein. Epilepsia 60, 1248–1254 (2019).

    PubMed  PubMed Central  Google Scholar 

  54. Brackhan, M. et al. [(18)F]GE180 positron emission tomographic imaging indicates a potential double-hit insult in the intrahippocampal kainate mouse model of temporal lobe epilepsy. Epilepsia 59, 617–626 (2018).

    Article  CAS  PubMed  Google Scholar 

  55. Bertoglio, D. et al. Non-invasive PET imaging of brain inflammation at disease onset predicts spontaneous recurrent seizures and reflects comorbidities. Brain Behav. Immun. 61, 69–79 (2017).

    Article  PubMed  Google Scholar 

  56. Russmann, V. et al. Identification of brain regions predicting epileptogenesis by serial [(18)F]GE-180 positron emission tomography imaging of neuroinflammation in a rat model of temporal lobe epilepsy. Neuroimage Clin. 15, 35–44 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Bar-Klein, G. et al. Imaging blood-brain barrier dysfunction as a biomarker for epileptogenesis. Brain 140, 1692–1705 (2017).

    Article  PubMed  Google Scholar 

  58. Breuer, H. et al. Multimodality imaging of blood-brain barrier impairment during epileptogenesis. J. Cereb. Blood Flow. Metab. 37, 2049–2061 (2017).

    Article  CAS  PubMed  Google Scholar 

  59. Koepp, M. J. et al. Neuroinflammation imaging markers for epileptogenesis. Epilepsia 58, 11–19 (2017).

    Article  PubMed  Google Scholar 

  60. Singh, A. K. et al. Dynamic contrast-enhanced (DCE) MRI derived kinetic perfusion indices may help predicting seizure control in single calcified neurocysticercosis. Magn. Reson. Imaging 49, 55–62 (2018).

    Article  PubMed  Google Scholar 

  61. Immonen, R. et al. Harmonization of pipeline for preclinical multicenter MRI biomarker discovery in a rat model of post-traumatic epileptogenesis. Epilepsy Res. 150, 46–57 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Levine, D., McDonald, R. J. & Kressel, H. Y. Gadolinium retention after contrast-enhanced MRI. JAMA 320, 1853–1854 (2018).

    Article  PubMed  Google Scholar 

  63. Janeczko, K., Kosonowska, E., Kiepura, A., Weglarz, W. & Setkowicz, Z. Volumetric response of the adult brain to seizures depends on the developmental stage when systemic inflammation was induced. Epilepsy Behav. 78, 280–287 (2018).

    Article  PubMed  Google Scholar 

  64. Andrade, P., Nissinen, J. & Pitkanen, A. Generalized seizures after experimental traumatic brain injury occur at the transition from slow-wave to rapid eye movement sleep. J. Neurotrauma 34, 1482–1487 (2017).

    Article  PubMed  Google Scholar 

  65. Bragin, A. et al. Pathologic electrographic changes after experimental traumatic brain injury. Epilepsia 57, 735–745 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Milikovsky, D. Z. et al. Electrocorticographic dynamics as a novel biomarker in five models of epileptogenesis. J. Neurosci. 37, 4450–4461 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Bahari, F., Ssentongo, P., Schiff, S. J. & Gluckman, B. J. A brain-heart biomarker for epileptogenesis. J. Neurosci. 38, 8473–8483 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Perucca, P., Smith, G., Santana-Gomez, C., Bragin, A. & Staba, R. Electrophysiological biomarkers of epileptogenicity after traumatic brain injury. Neurobiol. Dis. 123, 69–74 (2019).

    Article  PubMed  Google Scholar 

  69. Zijlmans, M. et al. High-frequency oscillations as a new biomarker in epilepsy. Ann. Neurol. 71, 169–178 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Menendez de la Prida, L., Staba, R. J. & Dian, J. A. Conundrums of high-frequency oscillations (80-800 Hz) in the epileptic brain. J. Clin. Neurophysiol. 32, 207–219 (2015).

    Article  PubMed  Google Scholar 

  71. Worrell, G. A. et al. Recording and analysis techniques for high-frequency oscillations. Prog. Neurobiol. 98, 265–278 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Kim, J. A. et al. Epileptiform activity in traumatic brain injury predicts post-traumatic epilepsy. Ann. Neurol. 83, 858–862 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Tubi, M. A. et al. Early seizures and temporal lobe trauma predict post-traumatic epilepsy: A longitudinal study. Neurobiol. Dis. 123, 115–121 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Wu, J. Y. et al. Clinical electroencephalographic biomarker for impending epilepsy in asymptomatic tuberous sclerosis complex infants. Pediatr. Neurol. 54, 29–34 (2016).

    Article  PubMed  Google Scholar 

  75. Jain, S. V. et al. Prediction of neonatal seizures in hypoxic-ischemic encephalopathy using electroencephalograph power analyses. Pediatr. Neurol. 67, 64–70.e2 (2017).

    Article  PubMed  Google Scholar 

  76. Besio, W. G. et al. High-frequency oscillations recorded on the scalp of patients with epilepsy using tripolar concentric ring electrodes. IEEE J. Transl. Eng. Health Med. 2, 2000111 (2014).

    Article  PubMed  Google Scholar 

  77. Broer, S. & Loscher, W. Novel combinations of phenotypic biomarkers predict development of epilepsy in the lithium-pilocarpine model of temporal lobe epilepsy in rats. Epilepsy Behav. 53, 98–107 (2015).

    Article  PubMed  Google Scholar 

  78. Pascente, R. et al. Cognitive deficits and brain myo-inositol are early biomarkers of epileptogenesis in a rat model of epilepsy. Neurobiol. Dis. 93, 146–155 (2016).

    Article  CAS  PubMed  Google Scholar 

  79. Pepe, M. S. et al. Phases of biomarker development for early detection of cancer. J. Natl Cancer Inst. 93, 1054–1061 (2001). A roadmap for identification of early biomarkers of cancer.

    Article  CAS  PubMed  Google Scholar 

  80. Frisoni, G. B. et al. Strategic roadmap for an early diagnosis of Alzheimer’s disease based on biomarkers. Lancet Neurol. 16, 661–676 (2017). A roadmap for identification of early biomarkers of Alzheimer disease.

    Article  PubMed  Google Scholar 

  81. Hampel, H. et al. Blood-based biomarkers for Alzheimer disease: mapping the road to the clinic. Nat. Rev. Neurol. 14, 639–652 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  82. International League Against Epilepsy Consortium on Complex Epilepsies. Genome-wide mega-analysis identifies 16 loci and highlights diverse biological mechanisms in the common epilepsies. Nat. Commun. 9, 5269 (2018).

    Article  Google Scholar 

  83. Oyrer, J. et al. Ion channels in genetic epilepsy: from genes and mechanisms to disease-targeted therapies. Pharmacol. Rev. 70, 142–173 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Pitkänen, A., Buckmaster, P., Galanopoulou, A. S. & Moshé, S. Models of Seizures and Epilepsy 2nd edn (Academic Press, 2017).

  85. Sculier, C., Gainza-Lein, M., Sanchez Fernandez, I. & Loddenkemper, T. Long-term outcomes of status epilepticus: a critical assessment. Epilepsia 59, 155–169 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Pujar, S. & Scott, R. C. Long-term outcomes after childhood convulsive status epilepticus. Curr. Opin. Pediatr. 31, 763–768 (2019).

    Article  PubMed  Google Scholar 

  87. Levesque, M., Avoli, M. & Bernard, C. Animal models of temporal lobe epilepsy following systemic chemoconvulsant administration. J. Neurosci. Methods 260, 45–52 (2016).

    Article  PubMed  Google Scholar 

  88. Frey, L. C. Epidemiology of posttraumatic epilepsy: a critical review. Epilepsia 44, 11–17 (2003).

    Article  PubMed  Google Scholar 

  89. Xu, T. et al. Risk factors for posttraumatic epilepsy: a systematic review and meta-analysis. Epilepsy Behav. 67, 1–6 (2017).

    Article  PubMed  Google Scholar 

  90. Klein, P. & Tyrlikova, I. No prevention or cure of epilepsy as yet. Neuropharmacology 168, 107762 (2020).

    Article  CAS  PubMed  Google Scholar 

  91. Xiong, Y., Mahmood, A. & Chopp, M. Animal models of traumatic brain injury. Nat. Rev. Neurosci. 14, 128–142 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Camilo, O. & Goldstein, L. B. Seizures and epilepsy after ischemic stroke. Stroke 35, 1769–1775 (2004).

    Article  PubMed  Google Scholar 

  93. Feyissa, A. M., Hasan, T. F. & Meschia, J. F. Stroke-related epilepsy. Eur. J. Neurol. 26, 18–e13 (2019).

    Article  CAS  PubMed  Google Scholar 

  94. Sommer, C. J. Ischemic stroke: experimental models and reality. Acta Neuropathol. 133, 245–261 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  95. Macrae, I. M. Preclinical stroke research–advantages and disadvantages of the most common rodent models of focal ischaemia. Br. J. Pharmacol. 164, 1062–1078 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Glass, H. C. et al. Risk factors for epilepsy in children with neonatal encephalopathy. Pediatr. Res. 70, 535–540 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  97. Liu, X., Jary, S., Cowan, F. & Thoresen, M. Reduced infancy and childhood epilepsy following hypothermia-treated neonatal encephalopathy. Epilepsia 58, 1902–1911 (2017).

    Article  PubMed  Google Scholar 

  98. Hamdy, N., Eide, S., Sun, H. S. & Feng, Z. P. Animal models for neonatal brain injury induced by hypoxic ischemic conditions in rodents. Exp. Neurol. 334, 113457 (2020).

    Article  PubMed  Google Scholar 

  99. Ramantani, G. & Holthausen, H. Epilepsy after cerebral infection: review of the literature and the potential for surgery. Epileptic Disord. 19, 117–136 (2017).

    Article  PubMed  Google Scholar 

  100. Vezzani, A. et al. Infections, inflammation and epilepsy. Acta Neuropathol. 131, 211–234 (2016).

    Article  CAS  PubMed  Google Scholar 

  101. Pallud, J. et al. Epileptic seizures in diffuse low-grade gliomas in adults. Brain 137, 449–462 (2014).

    Article  PubMed  Google Scholar 

  102. Kirschstein, T. & Kohling, R. Animal models of tumour-associated epilepsy. J. Neurosci. Methods 260, 109–117 (2016).

    Article  PubMed  Google Scholar 

  103. Geis, C., Planaguma, J., Carreno, M., Graus, F. & Dalmau, J. Autoimmune seizures and epilepsy. J. Clin. Invest. 129, 926–940 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  104. Shen, C. H. et al. Seizures and risk of epilepsy in anti-NMDAR, anti-LGI1, and anti-GABABR encephalitis. Ann. Clin. Transl. Neurol. 7, 1392–1399 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Taraschenko, O. et al. A mouse model of seizures in anti-N-methyl-D-aspartate receptor encephalitis. Epilepsia 60, 452–463 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Petit-Pedrol, M. et al. LGI1 antibodies alter Kv1.1 and AMPA receptors changing synaptic excitability, plasticity and memory. Brain 141, 3144–3159 (2018).

    PubMed  PubMed Central  Google Scholar 

  107. Choy, M. et al. A novel, noninvasive, predictive epilepsy biomarker with clinical potential. J. Neurosci. 34, 8672–8684 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The contents of this article are based on proposals made at a workshop organized by the National Institute of Neurological Disease and Stroke (NINDS) to accelerate therapies for anti-epileptogenesis and disease modification (Bethesda, MD, USA, 6th–8th August 2018). M.S. is supported in part by the European Community (FP7-HEALTH project 602102 [EPITARGET]). D.C.H. is supported by the Science Foundation Ireland (SFI) grant number 13/IA/1891 and 16/RC/3948 and co-funded under the European Regional Development Fund and by FutureNeuro industry partners. Other support to D.C.H. was from the European Union’s ‘Seventh Framework’ Programme (FP7) under Grant Agreement no. 602130 (EpimiRNA) and Medical Research Charities Group (2016-9). A.P. is supported by the Academy of Finland, European Community (FP7-HEALTH project 602102 [EPITARGET]), the National Institute of Neurological Disorders and Stroke (NINDS) Centers without Walls [grant number U54 NS100064]. W.H.T. is supported by the Division of Intramural Research, NINDS NIH. F.H.K. and K.K.W. are supported in part by a CURE grant received from the United States Army Medical Research and Materiel Command, Department of Defense (DoD), through the Psychological Health and Traumatic Brain Injury Research Program under award no. W81XWH-15-2-0069. K.S.W. is in part supported with Federal funds from the NINDS, National Institutes of Health, Department of Health and Human Services, under contract no. HHSN271201600048C.

Review criteria

We searched PubMed for articles published up to August 2020, including experimental and clinical studies published in English. Generic searches using free terms (such as “epilepsy” AND “biomarker”) had low specificity and retrieved a large number of articles (2,812 articles), the majority of which provided no clear evidence to support the notion that a particular molecular, imaging, electroencephalography (EEG) or behavioural alteration could be considered a biomarker of epilepsy or epileptogenesis. Use of medical subject heading (MeSH) terms (such as “Epilepsy”[Majr] AND “Biomarkers”[Majr]) had low sensitivity and retrieved few articles (149 articles). Similarly, unsatisfactory results were obtained when using the generic search “epileptogenesis” AND “biomarker” or “Biomarkers”[Majr] (339 and 45 articles, respectively). Therefore, we refined the generic search with specific biomarker types (molecular, imaging, EEG, behavioural) by adding one or more subtopic-specific additional query term. Eligible papers were then read and filtered on the basis of experimental methodology — studies that included a receiver operating characteristic (ROC) analysis or an equivalent methodology for ascertaining discriminative value were prioritized. In addition, articles from leaders in the field of epilepsy and epileptogenesis were included on the basis of the investigators’ expertise. Consequently, the final selection of biomarkers discussed in this Review comes from a narrative rather than a systematic search. For Table 1, rates and other information were compiled from reviews or, in some cases, original references identified by use of tailored search terms for each risk factor.

Author information

Authors and Affiliations

Authors

Contributions

The Review was envisioned and planned as a collaborative activity within the Biomarkers and Translational Science Working Group organized by the National Institute of Neurological Disease and Stroke within the initiative “Accelerating Therapies for Antiepileptogenesis and Disease Modification”. All authors contributed to the conception, design, literature search and writing for this Review.

Corresponding author

Correspondence to Michele Simonato.

Ethics declarations

Competing interest

K.S.W. is on the scientific advisory board for and is shareholder of Blackfynn, and is a consultant for Xenon Pharmaceuticals. All other authors declare no competing interests.

Additional information

Peer review information

Nature Reviews Neurology thanks I. Ali, R. Surges 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.

Related links

EpiBioS4Rx: https://epibios.loni.usc.edu

EpimiRNA: https://www.epimirna.eu

EPISTOP: http://epistop.eu

EPITARGET: https://www.epitarget.eu

Supplementary Information

Glossary

Receiver operating characteristic (ROC) analysis

A standard method to determine the sensitivity and specificity of a proposed biomarker.

Autoradiography

A technique in which an image is produced on an X-ray film or nuclear emulsion by the pattern of decay emissions from the distribution of a radioactive substance in a cellular or histological preparation.

Sleep spindles

Trains of distinct waves with frequency at 11–16 Hz detectable on the EEG during sleep.

Negative predictive value

The probability that a patient does not have the disease when the biomarker is negative.

Positive predictive value

The probability that a patient has the disease when the biomarker is positive.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Simonato, M., Agoston, D.V., Brooks-Kayal, A. et al. Identification of clinically relevant biomarkers of epileptogenesis — a strategic roadmap. Nat Rev Neurol 17, 231–242 (2021). https://doi.org/10.1038/s41582-021-00461-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41582-021-00461-4

This article is cited by

Search

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