癫痫的计算模型

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Computational models in epilepsy mainly focus on describing an electrophysiological manifestation associated with epilepsy called seizures. For this purpose, computational neurosciences use differential equations to reproduce the temporal evolution of the signals recorded experimentally. A book published in 2008, Computational Neuroscience in Epilepsy,.[1] summarizes different works done up to this time. The goals of using its models are diverse, from prediction to comprehension of underlying mechanisms.[2]

Computational models in epilepsy mainly focus on describing an electrophysiological manifestation associated with epilepsy called seizures. For this purpose, computational neurosciences use differential equations to reproduce the temporal evolution of the signals recorded experimentally. A book published in 2008, Computational Neuroscience in Epilepsy,. summarizes different works done up to this time. The goals of using its models are diverse, from prediction to comprehension of underlying mechanisms.

癫痫的计算模型主要集中在描述与癫痫发作有关的电生理表现。为此，计算神经科学使用微分方程来重现实验记录的信号的时间演变。2008年出版的一本书，《癫痫的计算神经科学》 ,。总结了到目前为止所做的不同工作。使用它的模型的目的是多种多样的，从预测到理解潜在的机制。

The crisis phenomenon (seizure) exists and shares certain dynamical properties across different scales[3] and different organisms.[4] It is possible to distinguish different approaches: the phenomenological models focus on the dynamics observed, generally reduced to few dimension it facilitates the study from the point of view of the theory of dynamical systems[5] and more mechanistic models that explain the biophysical interactions underlying seizures. It is also possible to use these approaches to model and analyse the interactions between different regions of the brain[6] (In this case the notion of network plays an important role[7]) and the transition to ictal state.[8] These large-scale approaches have the advantage of being able to be related to the recordings made in humans thanks to electroencephalography (EEG). It offers new directions for clinical research, particularly as an additional tool in the treatment of refractory epilepsy [9][10]

The crisis phenomenon (seizure) exists and shares certain dynamical properties across different scales and different organisms. It is possible to distinguish different approaches: the phenomenological models focus on the dynamics observed, generally reduced to few dimension it facilitates the study from the point of view of the theory of dynamical systems and more mechanistic models that explain the biophysical interactions underlying seizures. It is also possible to use these approaches to model and analyse the interactions between different regions of the brain (In this case the notion of network plays an important role) and the transition to ictal state. These large-scale approaches have the advantage of being able to be related to the recordings made in humans thanks to electroencephalography (EEG). It offers new directions for clinical research, particularly as an additional tool in the treatment of refractory epilepsy

危机现象(癫痫)存在于不同尺度和不同生物体之间，并具有一定的动力学特性。可以区分不同的方法: 现象学模型侧重于观察到的动力学，一般降低到很少的维度，这有利于从动力系统理论的角度进行研究和更多的解释癫痫发作的生物物理学相互作用的机制模型。也可以用这些方法来模拟和分析大脑不同区域之间的相互作用(在这种情况下，网络的概念起着重要作用)和向发作状态的过渡。这些大规模的方法有一个优势，那就是可以通过脑电图与人类的记录相关联。这多亏了脑电图。它为临床研究提供了新的方向，特别是作为治疗难治性癫痫的补充手段

Other approaches are to use the models to try to understand the mechanisms underlying these seizures using biophysical descriptions from the neuron scale.[11][12][13][14] This makes it possible to understand the role of homeostasis and to understand the link between physical quantities (such as the concentration of potassium for example) and the pathological dynamics observed.[citation needed]

Other approaches are to use the models to try to understand the mechanisms underlying these seizures using biophysical descriptions from the neuron scale. This makes it possible to understand the role of homeostasis and to understand the link between physical quantities (such as the concentration of potassium for example) and the pathological dynamics observed.

其他的方法是使用模型试图理解这些癫痫发作的机制，使用神经元尺度的生物物理描述。这使得理解稳态的作用和理解物理量(例如钾的浓度)和观察到的病理动力学之间的联系成为可能。

This area of research has evolved rapidly in recent years and continues to show promise for our understanding and treatment of epilepsies for either for direct clinical application in the case of refractory epilepsy or fundamental research to guide experimental works.[citation needed]

This area of research has evolved rapidly in recent years and continues to show promise for our understanding and treatment of epilepsies for either for direct clinical application in the case of refractory epilepsy or fundamental research to guide experimental works.

这一研究领域近年来发展迅速，并继续为我们理解和治疗癫痫显示出希望，无论是在难治性癫痫病例中的直接临床应用，还是指导实验工作的基础研究。

References

References

= 参考文献 =

  1. ↑ Computational neuroscience in epilepsy. Ivan Soltesz, Kevin Staley (1st ed.). Amsterdam: Academic. 2008. ISBN 978-0-12-373649-9. OCLC 281558250. https://www.worldcat.org/oclc/281558250. 
  2. ↑ Lytton, William W. (August 2008). "Computer modelling of epilepsy". Nature Reviews Neuroscience (in English). 9 (8): 626–637. doi:10.1038/nrn2416. ISSN 1471-0048. PMC 2739976. PMID 18594562.
  3. ↑ Depannemaecker, Damien; Destexhe, Alain; Jirsa, Viktor; Bernard, Christophe (2021-02-22). "Modeling Seizures: From Single Neurons to Networks". doi:10.20944/preprints202102.0478.v1.
  4. ↑ Jirsa, Viktor K.; Stacey, William C.; Quilichini, Pascale P.; Ivanov, Anton I.; Bernard, Christophe (2014-06-10). "On the nature of seizure dynamics". Brain. 137 (8): 2210–2230. doi:10.1093/brain/awu133. ISSN 1460-2156. PMC 4107736. PMID 24919973.
  5. ↑ Saggio, Maria Luisa; Spiegler, Andreas; Bernard, Christophe; Jirsa, Viktor K. (2017-07-25). "Fast–Slow Bursters in the Unfolding of a High Codimension Singularity and the Ultra-slow Transitions of Classes". The Journal of Mathematical Neuroscience. 7 (1): 7. doi:10.1186/s13408-017-0050-8. ISSN 2190-8567. PMC 5526832. PMID 28744735.
  6. ↑ Breakspear, M.; Roberts, J. A.; Terry, J. R.; Rodrigues, S.; Mahant, N.; Robinson, P. A. (2005-11-09). "A Unifying Explanation of Primary Generalized Seizures Through Nonlinear Brain Modeling and Bifurcation Analysis". Cerebral Cortex. 16 (9): 1296–1313. doi:10.1093/cercor/bhj072. ISSN 1460-2199. PMID 16280462.
  7. ↑ Terry, John R.; Benjamin, Oscar; Richardson, Mark P. (2012). "Seizure generation: The role of nodes and networks". Epilepsia (in English). 53 (9): e166–e169. doi:10.1111/j.1528-1167.2012.03560.x. ISSN 1528-1167. PMID 22709380. S2CID 25085531.
  8. ↑ Wendling, Fabrice; Hernandez, Alfredo; Bellanger, Jean-Jacques; Chauvel, Patrick; Bartolomei, Fabrice (October 2005). "Interictal to ictal transition in human temporal lobe epilepsy: insights from a computational model of intracerebral EEG". Journal of Clinical Neurophysiology. 22 (5): 343–356. ISSN 0736-0258. PMC 2443706. PMID 16357638.
  9. ↑ Jirsa, V.K.; Proix, T.; Perdikis, D.; Woodman, M.M.; Wang, H.; Gonzalez-Martinez, J.; Bernard, C.; Bénar, C.; Guye, M.; Chauvel, P.; Bartolomei, F. (2017-01-15). "The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread". NeuroImage (in English). 145 (Pt B): 377–388. doi:10.1016/j.neuroimage.2016.04.049. ISSN 1053-8119. PMID 27477535. S2CID 36510741.
  10. ↑ Khambhati, Ankit N.; Davis, Kathryn A.; Lucas, Timothy H.; Litt, Brian; Bassett, Danielle S. (September 2016). "Virtual Cortical Resection Reveals Push-Pull Network Control Preceding Seizure Evolution". Neuron (in English). 91 (5): 1170–1182. doi:10.1016/j.neuron.2016.07.039. PMC 5017915. PMID 27568515.
  11. ↑ 模板:Cite bioRxiv
  12. ↑ Cressman, John R.; Ullah, Ghanim; Ziburkus, Jokubas; Schiff, Steven J.; Barreto, Ernest (April 2009). "The influence of sodium and potassium dynamics on excitability, seizures, and the stability of persistent states: I. Single neuron dynamics". Journal of Computational Neuroscience (in English). 26 (2): 159–170. doi:10.1007/s10827-008-0132-4. ISSN 0929-5313. PMC 2704057. PMID 19169801.
  13. ↑ Destexhe, A.; Bal, T.; McCormick, D. A.; Sejnowski, T. J. (1996-09-01). "Ionic mechanisms underlying synchronized oscillations and propagating waves in a model of ferret thalamic slices". Journal of Neurophysiology (in English). 76 (3): 2049–2070. doi:10.1152/jn.1996.76.3.2049. ISSN 0022-3077.
  14. ↑ Almeida, Antônio-Carlos G. De; Rodrigues, Antônio M.; Scorza, Fúlvio A.; Cavalheiro, Esper A.; Teixeira, Hewerson Z.; Duarte, Mário A.; Silveira, Gilcélio A.; Arruda, Emerson Z. (2008). "Mechanistic hypotheses for nonsynaptic epileptiform activity induction and its transition from the interictal to ictal state—Computational simulation". Epilepsia (in English). 49 (11): 1908–1924. doi:10.1111/j.1528-1167.2008.01686.x. ISSN 1528-1167. PMID 18513350. S2CID 12024463.

Category:Computational biology Category:Epilepsy

类别: 计算生物学类别: 癫痫


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