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用于定位癫痫发作起始区的诱发电位状态空间模型。

State-space models of evoked potentials to localize the seizure onset zone.

作者信息

Smith Rachel J, Kamali Golnoosh, Hays Mark, Coogan Christopher G, Crone Nathan E, Sarma Sridevi V, Kang Joon Y

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:2528-2531. doi: 10.1109/EMBC44109.2020.9176697.

Abstract

Surgical removal of the seizure onset zone (SOZ) in epilepsy patients is a potentially curative treatment, but the process heavily relies on accurate localization of the SOZ via visual inspection. SPES (Single-pulse electrical stimulation) is a method recently used to explore inter-areal connectivity in vivo to probe functional brain networks such as language and motor networks, and to a much lesser degree, seizure networks. We hypothesized that a dynamical quantification of the connectivity networks derived from the evoked responses induced by SPES could also be used to localize the SOZ. To test our hypothesis, we used an intracranial EEG (iEEG) data set in which five epilepsy patients underwent extensive SPES evaluation. For each patient, and for each dataset that stimulated a different pair of electrodes, we constructed a state-space model from the patient's data. Specifically, we simultaneously estimated model parameters under an exogenous pulse input to a dynamical system whose state vector consisted of the response iEEG signals. Then, the size of the reachable state space, as quantified by the maximum singular value of the reachability matrix, σ(R), was computed and denoted as the "largest" network response possible when stimulating the given pair. Our results suggest high agreement between σ(R) and clinically annotated SOZ for patients with localizable SOZs.Clinical Relevance- Our study applies dynamical systems theory to identify epileptogenic brain regions, creating a novel tool that clinicians may use in surgical planning for medically-refractory epilepsy patients.

摘要

手术切除癫痫患者的癫痫发作起始区(SOZ)是一种潜在的治愈性治疗方法,但该过程严重依赖于通过视觉检查对SOZ进行精确定位。单脉冲电刺激(SPES)是一种最近用于在体内探索区域间连接性以探测功能性脑网络(如语言和运动网络)的方法,在较小程度上也可用于探测癫痫网络。我们假设,对由SPES诱发的反应所衍生的连接网络进行动态量化,也可用于定位SOZ。为了验证我们的假设,我们使用了一个颅内脑电图(iEEG)数据集,其中五名癫痫患者接受了广泛的SPES评估。对于每位患者以及刺激不同电极对的每个数据集,我们根据患者的数据构建了一个状态空间模型。具体而言,我们在一个动态系统的外部脉冲输入下同时估计模型参数,该动态系统的状态向量由反应性iEEG信号组成。然后,计算由可达性矩阵的最大奇异值σ(R)量化的可达状态空间的大小,并将其表示为刺激给定电极对时可能的“最大”网络反应。我们的结果表明,对于具有可定位SOZ的患者,σ(R)与临床标注的SOZ之间高度一致。临床意义——我们的研究应用动态系统理论来识别致痫性脑区,创建了一种临床医生可用于难治性癫痫患者手术规划的新工具。

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