Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Epilepsia. 2020 Nov;61(11):2534-2544. doi: 10.1111/epi.16686. Epub 2020 Sep 18.
In patients with medically refractory focal epilepsy, stereotactic-electroencephalography (SEEG) can aid in localizing epileptogenic regions for surgical treatment. SEEG, however, requires long hospitalizations to record seizures, and ictal interpretation can be incomplete or inaccurate. Our recent work showed that non-directed resting-state analyses may identify brain regions as epileptogenic or uninvolved. Our present objective is to map epileptogenic networks in greater detail and more accurately identify seizure-onset regions using directed resting-state SEEG connectivity.
In 25 patients with focal epilepsy who underwent SEEG, 2 minutes of resting-state, artifact-free, SEEG data were selected and functional connectivity was estimated. Using standard clinical interpretation, brain regions were classified into four categories: ictogenic, early propagation, irritative, or uninvolved. Three non-directed connectivity measures (mutual information [MI] strength, and imaginary coherence between and within regions) and four directed measures (partial directed coherence [PDC] and directed transfer function [DTF], inward and outward strength) were calculated. Logistic regression was used to generate a predictive model of ictogenicity.
Ictogenic regions had the highest and uninvolved regions had the lowest MI strength. Although both PDC and DTF inward strengths were highest in ictogenic regions, outward strengths did not differ among categories. A model incorporating directed and nondirected connectivity measures demonstrated an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.88 in predicting ictogenicity of individual regions. The AUC of this model was 0.93 when restricted to patients with favorable postsurgical seizure outcomes.
Directed connectivity measures may help identify epileptogenic networks without requiring ictal recordings. Greater inward but not outward connectivity in ictogenic regions at rest may represent broad inhibitory input to prevent seizure generation.
在药物难治性局灶性癫痫患者中,立体定向脑电图(SEEG)可辅助定位致痫区以进行手术治疗。然而,SEEG 需要长时间住院以记录发作,且发作期解读可能不完整或不准确。我们最近的研究表明,非定向静息态分析可能有助于识别致痫或非致痫脑区。本研究旨在通过定向静息态 SEEG 连接更详细地绘制致痫网络,并更准确地识别发作起始区。
对 25 例接受 SEEG 检查的局灶性癫痫患者,选择 2 分钟无伪迹的静息态 SEEG 数据,并估计功能连接。采用标准临床解读方法,将脑区分为 4 类:致痫区、早期传播区、刺激性区和非致痫区。计算了 3 种非定向连接度量(互信息[MI]强度和区域间和区域内的虚相干)和 4 种定向连接度量(部分定向相干[PDC]和定向传递函数[DTF],内向和外向强度)。采用逻辑回归生成致痫性预测模型。
致痫区的 MI 强度最高,非致痫区最低。虽然致痫区的 PDC 和 DTF 内向强度最高,但各分类之间的外向强度没有差异。纳入定向和非定向连接度量的模型在预测单个区域致痫性方面的受试者工作特征(ROC)曲线下面积(AUC)为 0.88。当限制于术后癫痫发作结局良好的患者时,该模型的 AUC 为 0.93。
定向连接度量可能有助于在无需记录发作的情况下识别致痫网络。致痫区在静息时的内向连接增强而外向连接无差异,可能代表广泛的抑制性输入以防止发作发生。