Dynamic causal modelling of electrographic seizure activity using Bayesian belief updating.
Dynamic causal modelling of electrographic seizure activity using Bayesian belief updating.
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DOI：
10.1016/j.neuroimage.2015.07.063
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发表时间：
2016-01-15
期刊：
影响因子：
5.7
通讯作者：
Friston K
中科院分区：
文献类型：
--
作者：
Cooray GK;Sengupta B;Douglas PK;Friston K
脑电图记录中的癫痫发作活动可以持续数小时，并且癫痫发作动态随着时间和空间的变化而迅速变化。为了表征癫痫发作活动的时空演变，通常需要分析大型数据集。动态因果模型（DCM）可用于估计癫痫发作期间皮质动力学的突触驱动因素；然而，必要的（贝叶斯）反演过程的计算成本很高。在这篇文章中，我们描述了在 DCM 框架内的一个简单的程序，该程序提供了通过非侵入性和侵入性生理记录测量的癫痫活动的有效反转；即脑电图/心电图。我们描述了 DCM 贝叶斯信念更新方案背后的理论背景。该方案在模拟和经验癫痫发作活动（侵入性和非侵入性记录）上进行测试，并与标准贝叶斯反演进行比较。我们表明，与标准方案相比，贝叶斯信念更新方案提供了类似的时变突触参数估计，表明准确性没有显着的质变。所解释的方差差异很小（小于 5%）。更新方法的效率要高得多，大约需要 5-10 分钟，而不是大约 1-2 小时。此外，更新方案下的模型设置可以清楚地说明神经元变量在可分离时间尺度上如何波动。现在，这种方法使我们能够研究快速（神经元）活动对（突触）参数缓慢波动的影响，为了解癫痫活动如何产生铺平了道路。我们描述了一种 DCM 程序，可以有效反转癫痫活动。与标准 DCM 方法相比，精度相似，但效率更高。可以指定不同时间尺度的生理波动。该方案应有助于使用 DCM 了解癫痫发作活动。
Seizure activity in EEG recordings can persist for hours with seizure dynamics changing rapidly over time and space. To characterise the spatiotemporal evolution of seizure activity, large data sets often need to be analysed. Dynamic causal modelling (DCM) can be used to estimate the synaptic drivers of cortical dynamics during a seizure; however, the requisite (Bayesian) inversion procedure is computationally expensive. In this note, we describe a straightforward procedure, within the DCM framework, that provides efficient inversion of seizure activity measured with non-invasive and invasive physiological recordings; namely, EEG/ECoG. We describe the theoretical background behind a Bayesian belief updating scheme for DCM. The scheme is tested on simulated and empirical seizure activity (recorded both invasively and non-invasively) and compared with standard Bayesian inversion. We show that the Bayesian belief updating scheme provides similar estimates of time-varying synaptic parameters, compared to standard schemes, indicating no significant qualitative change in accuracy. The difference in variance explained was small (less than 5%). The updating method was substantially more efficient, taking approximately 5–10 min compared to approximately 1–2 h. Moreover, the setup of the model under the updating scheme allows for a clear specification of how neuronal variables fluctuate over separable timescales. This method now allows us to investigate the effect of fast (neuronal) activity on slow fluctuations in (synaptic) parameters, paving a way forward to understand how seizure activity is generated. We describe a DCM procedure that provides efficient inversion of seizure activity. Similar accuracy but substantially more efficient compared to standard DCM methods. Physiological fluctuations over different timescales can be specified. This scheme should contribute to understanding seizure activity using DCM.