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发作间期 EEG-fMRI 检测不同统计阈值下的发作起始区的敏感性和特异性。

Sensitivity and Specificity of Interictal EEG-fMRI for Detecting the Ictal Onset Zone at Different Statistical Thresholds.

机构信息

Laboratory for Epilepsy Research, UZ Leuven and KU Leuven , Leuven , Belgium ; Medical Imaging Research Center, UZ Leuven and KU Leuven , Leuven , Belgium.

Laboratory for Epilepsy Research, UZ Leuven and KU Leuven , Leuven , Belgium ; Medical Imaging Research Center, UZ Leuven and KU Leuven , Leuven , Belgium ; Laboratory for Cognitive Neurology, UZ Leuven and KU Leuven , Leuven , Belgium.

出版信息

Front Neurol. 2014 Jul 17;5:131. doi: 10.3389/fneur.2014.00131. eCollection 2014.

Abstract

There is currently a lack of knowledge about electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) specificity. Our aim was to define sensitivity and specificity of blood oxygen level dependent (BOLD) responses to interictal epileptic spikes during EEG-fMRI for detecting the ictal onset zone (IOZ). We studied 21 refractory focal epilepsy patients who had a well-defined IOZ after a full presurgical evaluation and interictal spikes during EEG-fMRI. Areas of spike-related BOLD changes overlapping the IOZ in patients were considered as true positives; if no overlap was found, they were treated as false-negatives. Matched healthy case-controls had undergone similar EEG-fMRI in order to determine true-negative and false-positive fractions. The spike-related regressor of the patient was used in the design matrix of the healthy case-control. Suprathreshold BOLD changes in the brain of controls were considered as false positives, absence of these changes as true negatives. Sensitivity and specificity were calculated for different statistical thresholds at the voxel level combined with different cluster size thresholds and represented in receiver operating characteristic (ROC)-curves. Additionally, we calculated the ROC-curves based on the cluster containing the maximal significant activation. We achieved a combination of 100% specificity and 62% sensitivity, using a Z-threshold in the interval 3.4-3.5 and cluster size threshold of 350 voxels. We could obtain higher sensitivity at the expense of specificity. Similar performance was found when using the cluster containing the maximal significant activation. Our data provide a guideline for different EEG-fMRI settings with their respective sensitivity and specificity for detecting the IOZ. The unique cluster containing the maximal significant BOLD activation was a sensitive and specific marker of the IOZ.

摘要

目前,人们对脑电图(EEG)-功能磁共振成像(fMRI)的特异性知之甚少。我们的目的是确定 BOLD 响应对 EEG-fMRI 中癫痫发作间期棘波的敏感性和特异性,以检测发作起始区(IOZ)。我们研究了 21 例难治性局灶性癫痫患者,这些患者在全面术前评估后具有明确的 IOZ 和 EEG-fMRI 中的发作间期棘波。在患者中与棘波相关的 BOLD 变化区域与 IOZ 重叠的被认为是真阳性;如果未发现重叠,则被视为假阴性。匹配的健康对照组进行了类似的 EEG-fMRI,以确定真阴性和假阳性分数。患者的棘波相关回归器被用于健康对照组的设计矩阵。大脑中超过阈值的 BOLD 变化被认为是假阳性,没有这些变化被认为是真阴性。在体素水平上计算不同统计阈值的敏感性和特异性,结合不同的簇大小阈值,并以受试者工作特征(ROC)曲线表示。此外,我们还基于包含最大显著激活的簇计算了 ROC 曲线。我们使用 Z 阈值在 3.4-3.5 之间和簇大小阈值为 350 体素的情况下,实现了 100%特异性和 62%敏感性的组合。我们可以牺牲特异性来获得更高的敏感性。使用包含最大显著激活的簇时,也发现了类似的性能。我们的数据为不同的 EEG-fMRI 设置提供了一个指南,这些设置具有各自的敏感性和特异性,用于检测 IOZ。包含最大显著 BOLD 激活的独特簇是 IOZ 的敏感和特异性标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fb8/4101337/ec08457c49a1/fneur-05-00131-g001.jpg

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