Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography.
Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography.
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从颅内脑电图中推断癫痫发作传播模式的数据驱动方法。
DOI：
10.1371/journal.pcbi.1008689
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发表时间：
2021-03
影响因子：
4.3
通讯作者：
Jirsa VK
中科院分区：
文献类型：
--
作者：
Sip V;Hashemi M;Vattikonda AN;Woodman MM;Wang H;Scholly J;Medina Villalon S;Guye M;Bartolomei F;Jirsa VK
针对癫痫患者进行旨在去除致痫区的手术干预的成功率仅为 60-70%。这种失败的部分原因是临床评估过程中植入的颅内电极的空间采样不足，导致未直接观察的区域的时空癫痫组织图像不完整。利用癫痫发作通过大脑网络传播的部分观察，并辅以癫痫发作沿着结构连接传播的假设，我们推断未观察到的区域是否以及何时在癫痫发作中被招募。为此，我们引入了一种数据驱动的癫痫招募和跨加权网络传播模型，我们使用贝叶斯推理框架对其进行反转。对 45 名患者的队列使用留一交叉验证方案，我们证明，与不使用结构信息的经验估计相比，该方法可以改进对未观察区域状态的预测，但它与考虑结构的估计处于同一水平。此外，与进行的手术切除和手术结果的比较表明推断的兴奋区域和实际的致癫痫区域之间的联系。结果强调了结构连接组在癫痫发作的大规模时空组织中的重要性，并引入了一种将患者特异性连接组和颅内癫痫记录整合到癫痫传播的全脑计算模型中的新方法。癫痫发作期间大脑的电活动可以通过颅内脑电图（即植入患者大脑的电极）来观察。然而，由于实际限制，只能植入选定的大脑区域，这带来了观察者隐藏某些非植入区域的异常电活动的风险。在这项工作中，我们介绍了一种根据癫痫发作的不完整观察来推断未观察部分发生的情况的方法。该方法依赖于癫痫沿着白质结构连接传播的假设，并找到与数据一致的全脑癫痫传播的解释。结构连接组可以通过个体患者的弥散加权成像来估计，因此，通过这种方式，可以利用患者特定的结构连接组来更好地分析患者的癫痫记录。
Surgical interventions in epileptic patients aimed at the removal of the epileptogenic zone have success rates at only 60-70%. This failure can be partly attributed to the insufficient spatial sampling by the implanted intracranial electrodes during the clinical evaluation, leading to an incomplete picture of spatio-temporal seizure organization in the regions that are not directly observed. Utilizing the partial observations of the seizure spreading through the brain network, complemented by the assumption that the epileptic seizures spread along the structural connections, we infer if and when are the unobserved regions recruited in the seizure. To this end we introduce a data-driven model of seizure recruitment and propagation across a weighted network, which we invert using the Bayesian inference framework. Using a leave-one-out cross-validation scheme on a cohort of 45 patients we demonstrate that the method can improve the predictions of the states of the unobserved regions compared to an empirical estimate that does not use the structural information, yet it is on the same level as the estimate that takes the structure into account. Furthermore, a comparison with the performed surgical resection and the surgery outcome indicates a link between the inferred excitable regions and the actual epileptogenic zone. The results emphasize the importance of the structural connectome in the large-scale spatio-temporal organization of epileptic seizures and introduce a novel way to integrate the patient-specific connectome and intracranial seizure recordings in a whole-brain computational model of seizure spread. The electrical activity of the brain during an epileptic seizure can be observed with intracranial EEG, that is electrodes implanted in the patient’s brain. However, due to the practical constraints only selected brain regions can be implanted, which brings a risk that the abnormal electrical activity in some non-implanted regions is hidden from the observers. In this work we introduce a method to infer what is happening in the unobserved parts based on the incomplete observations of the epileptic seizure. The method relies on the assumption that the seizure spreads along the white-matter structural connections, and finds the explanation of the whole-brain seizure spread consistent with the data. The structural connectome can be estimated from diffusion-weighted imaging for an individual patient, therefore this way the patient-specific structural connectome is utilized to better analyze the patients’ seizure recordings.
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影响因子：
5.7
作者：
Cooray GK;Sengupta B;Douglas PK;Friston K
通讯作者：
Friston K
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作者：
Hebbink, Jurgen;Meijer, Hil;Leijten, Frans
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Seeck, Margitta
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作者：
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