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将癫痫发作动力学建模为时空模式的重要性。

The importance of modeling epileptic seizure dynamics as spatio-temporal patterns.

作者信息

Baier Gerold, Goodfellow Marc, Taylor Peter N, Wang Yujiang, Garry Daniel J

机构信息

DTC Integrative Systems Biology, Manchester Interdisciplinary Biocentre, The University of Manchester Manchester, UK.

出版信息

Front Physiol. 2012 Jul 17;3:281. doi: 10.3389/fphys.2012.00281. eCollection 2012.

Abstract

The occurrence of seizures is the common feature across the spectrum of epileptic disorders. We describe how the use of mechanistic neural population models leads to novel insight into the dynamic mechanisms underlying two important types of epileptic seizures. We specifically stress the need for a spatio-temporal description of the rhythms to deal with the complexity of the pathophenotype. Adapted to functional and structural patient data, the macroscopic models may allow a patient-specific description of seizures and prediction of treatment outcome.

摘要

癫痫发作的出现是各种癫痫疾病的共同特征。我们描述了如何使用机械神经群体模型来深入了解两种重要类型癫痫发作背后的动态机制。我们特别强调需要对节律进行时空描述,以应对病理表型的复杂性。适应患者的功能和结构数据后,宏观模型可能允许对癫痫发作进行个体化描述并预测治疗结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a42/3429055/a4f3c82a1f1f/fphys-03-00281-g0001.jpg

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