Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UK.
Eur J Neurosci. 2012 Jul;36(2):2188-200. doi: 10.1111/j.1460-9568.2012.08082.x.
Focal-onset seizures have traditionally been conceptualised as having a highly circumscribed onset in an 'abnormal' brain region, with evolution of the seizure requiring recruitment of adjacent or connected 'normal' brain regions. Complementing this concept of the spatial evolution of seizures, the purpose of our present paper is to explore the evidence for the dynamic evolution of focal epilepsy using bifurcation analysis of a neural mass model, and subsequently relating these bifurcations to specific features of clinical data recordings in the time domain. Our study was motivated by recent research in idiopathic generalised epilepsies which has suggested that the temporal evolution of seizures may arise out of gradual changes in underlying physiological mechanisms. We found that spikes in the considered model arose out of so-called 'false' bifurcations, a finding consistent with other neural models with two timescales of inhibitory processes. We compared these results with previous studies of the model before extending them to characterise other more complex model behaviours. We then explored the relationship between model dynamics and clinical recordings from patients with focal epilepsies. Here we used an algorithm to subdivide each recording into time windows for which the data is approximately stationary. We then used our bifurcation findings to map out the evolution of seizures based on features of the clinical data from each of the epochs identified by our algorithm. We finally explored the similarity of the seizure evolution within patients, using random surrogates to test for statistical significance.
局灶性起始发作传统上被认为是在“异常”脑区具有高度局限性起始，随着发作的发展，需要募集相邻或相连的“正常”脑区。本研究旨在使用神经质量模型的分岔分析来探索局灶性癫痫动态演变的证据，补充了发作的空间演变的概念，并随后将这些分岔与时域中临床数据记录的特定特征相关联。我们的研究受到特发性全面性癫痫最近研究的启发，这些研究表明发作的时间演变可能源于潜在生理机制的逐渐变化。我们发现所考虑的模型中的尖峰源于所谓的“虚假”分岔，这一发现与具有两个抑制过程时间尺度的其他神经模型一致。我们将这些结果与模型的先前研究进行了比较，然后将其扩展到表征其他更复杂的模型行为。然后，我们探讨了模型动力学与局灶性癫痫患者临床记录之间的关系。在这里，我们使用一种算法将每个记录划分为数据近似稳定的时间窗口。然后，我们使用我们的分岔发现根据我们算法确定的每个时期的临床数据的特征来绘制发作的演变。最后，我们使用随机替代物来测试统计显著性，探索患者内发作演变的相似性。
