The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Computer modelling of epilepsy
Open accessAbstract
Epilepsy is a complex set of disorders that can involve many areas of the cortex, as well as underlying deep-brain systems. The myriad manifestations of seizures, which can be as varied as déjà vu and olfactory hallucination, can therefore give researchers insights into regional functions and relations. Epilepsy is also complex genetically and pathophysiologically: it involves microscopic (on the scale of ion channels and synaptic proteins), macroscopic (on the scale of brain trauma and rewiring) and intermediate changes in a complex interplay of causality. It has long been recognized that computer modelling will be required to disentangle causality, to better understand seizure spread and to understand and eventually predict treatment efficacy. Over the past few years, substantial progress has been made in modelling epilepsy at levels ranging from the molecular to the socioeconomic. We review these efforts and connect them to the medical goals of understanding and treating the disorder.
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Cites
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Similar network activity from disparate circuit parameters
Prinz, A. A., Bucher, D., & Marder, E. (2004) · Nature Neuroscience · doi:10.1038/nn1352 · Cited by 1,091
Modeling the Heart--from Genes to Cells to the Whole Organ
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“Much effort in detailed modelling has taken place at the network level, following pioneering studies of network activity in MTLE model”
Channelopathies as a genetic cause of epilepsy
Mulley, J. C., Scheffer, I. E., Petrou, S., & Berkovic, S. F. (2003) · Current Opinion in Neurology · doi:10.1097/00019052-200304000-00009 · Cited by 214
Parallel network simulations with NEURON
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Self-citationAggravation of epilepsy by anti-epileptic drugs
Gayatri, N., & Livingston, J. (2006) · Developmental Medicine & Child Neurology · doi:10.1017/s0012162206000843 · Cited by 114
Cited by
Hidden attractors in dynamical systems
Dudkowski, D., Jafari, S., Kapitaniak, T., Kuznetsov, N. V., Leonov, G. A., & Prasad, A. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.05.002 · Cited by 695
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Seizure prediction – ready for a new era
Kuhlmann, L., Lehnertz, K., Richardson, M. P., Schelter, B., & Zaveri, H. P. (2018) · Nature Reviews Neurology · doi:10.1038/s41582-018-0055-2 · Cited by 450
Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review
Rasheed, K., Qayyum, A., Qadir, J., Sivathamboo, S., Kwan, P., Kuhlmann, L., O'Brien, T., & Razi, A. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.3008792 · Cited by 409
Peña-Gómez, C., Sala-Lonch, R., Junqué, C., Clemente, I. C., Vidal, D., Bargalló, N., Falcón, C., Valls-Solé, J., Pascual-Leone, Á., & Bartrés-Faz, D. (2012) · Brain Stimulation · doi:10.1016/j.brs.2011.08.006 · Cited by 288
Metadata
- Authors
- Lytton, W. W.
- Venue
- Nature Reviews Neuroscience
- Published
- 2008-07-02
- Volume
- 9
- Issue
- 8
- Pages
- 626-637
- ISSN
- 1471-003X (ISSN-L)
- 1471-0048
- OpenAlex
- W2080929714
- Cited by
- 284
- References
- 9
- FWCI
- 1.59
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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