ABSTRACT We propose a new source connectivity method by focusing on estimating time courses of the regions of interest (ROIs). To this aim, it is necessary to consider the strong inherent non-stationary behavior of neural activity. We develop an iterative dynamic approach to extract a single time course for each ROI encoding the temporal non-stationary features. The proposed approach explicitly includes dynamic constraints by taking into account the evolution of the sources activities for further dynamic connectivity analysis. We simulated an epileptic network with a non-stationary structure; accordingly, EEG source reconstruction using LORETA is performed. Using the reconstructed sources, the spatially compact ROIs are selected. Then, a single time course encoding the temporal non-stationarity is extracted for each ROI. An adaptive directed transfer function (ADTF) is applied to measure the information flow of underlying brain networks. Obtained results demonstrate that the contributed approach is more efficient to estimate the ROI time series and ROI to ROI information flow in comparison with existing methods. Our work is validated in three drug-resistance epilepsy patients. The proposed ROI time series estimation directly affects the quality of connectivity analysis, leading to the best possible seizure onset zone (SOZ) localization verified by electrocorticography and post-operational results.

中文翻译：

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基于感兴趣区域动态时间序列估计的癫痫源连通性分析

摘要 我们提出了一种新的源连接方法，重点是估计感兴趣区域 (ROI) 的时间过程。为此，有必要考虑神经活动的强固有非平稳行为。我们开发了一种迭代动态方法，为每个 ROI 编码时间非平稳特征提取单个时间过程。所提出的方法通过考虑源活动的演变来明确包括动态约束，以进行进一步的动态连接分析。我们模拟了一个非平稳结构的癫痫网络；因此，执行使用 LORETA 的 EEG 源重建。使用重建的源，选择空间紧凑的 ROI。然后，为每个 ROI 提取编码时间非平稳性的单个时间过程。自适应定向传递函数 (ADTF) 用于测量底层大脑网络的信息流。获得的结果表明，与现有方法相比，贡献方法更有效地估计 ROI 时间序列和 ROI 到 ROI 信息流。我们的工作在三名耐药性癫痫患者中得到验证。所提出的 ROI 时间序列估计直接影响连接分析的质量，从而导致通过皮层电图和术后结果验证的最佳癫痫发作区 (SOZ) 定位。我们的工作在三名耐药性癫痫患者中得到了验证。所提出的 ROI 时间序列估计直接影响连接分析的质量，从而导致通过皮层电图和术后结果验证的最佳癫痫发作区 (SOZ) 定位。我们的工作在三名耐药性癫痫患者中得到了验证。所提出的 ROI 时间序列估计直接影响连接分析的质量，从而导致通过皮层电图和术后结果验证的最佳癫痫发作区 (SOZ) 定位。