Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Epilepsia. 2023 May;64(5):1305-1317. doi: 10.1111/epi.17565. Epub 2023 Mar 20.
Temporal lobe epilepsy (TLE) is the most common type of focal epilepsy. An increasingly identified subset of patients with TLE consists of those who show bilaterally independent temporal lobe seizures. The purpose of this study was to leverage network neuroscience to better understand the interictal whole brain network of bilateral TLE (BiTLE).
In this study, using a multicenter resting state functional magnetic resonance imaging (rs-fMRI) data set, we constructed whole-brain functional networks of 19 patients with BiTLE, and compared them to those of 75 patients with unilateral TLE (UTLE). We quantified resting-state, whole-brain topological properties using metrics derived from network theory, including clustering coefficient, global efficiency, participation coefficient, and modularity. For each metric, we computed an average across all brain regions, and iterated this process across network densities. Curves of network density vs each network metric were compared between groups. Finally, we derived a combined metric, which we term the "integration-segregation axis," by combining whole-brain average clustering coefficient and global efficiency curves, and applying principal component analysis (PCA)-based dimensionality reduction.
Compared to UTLE, BiTLE had decreased global efficiency (p = .031), and decreased whole brain average participation coefficient across a range of network densities (p = .019). Modularity maximization yielded a larger number of smaller communities in BiTLE than in UTLE (p = .020). Differences in network properties separate BiTLE and UTLE along the integration-segregation axis, with regions within the axis having a specificity of up to 0.87 for BiTLE. Along the integration-segregation axis, UTLE patients with poor surgical outcomes were distributed in the same regions as BiTLE, and network metrics confirmed similar patterns of increased segregation in both BiTLE and poor outcome UTLE.
Increased interictal whole-brain network segregation, as measured by rs-fMRI, is specific to BiTLE, as well as poor surgical outcome UTLE, and may assist in non-invasively identifying this patient population prior to intracranial electroencephalography or device implantation.
颞叶癫痫（TLE）是最常见的局灶性癫痫类型。越来越多的 TLE 患者亚组是那些表现出双侧颞叶独立发作的患者。本研究旨在利用网络神经科学更好地理解双侧颞叶癫痫（BiTLE）的发作间期全脑网络。
在这项研究中，我们使用多中心静息状态功能磁共振成像（rs-fMRI）数据集，构建了 19 例 BiTLE 患者的全脑功能网络，并将其与 75 例单侧颞叶癫痫（UTLE）患者的网络进行比较。我们使用网络理论衍生的度量标准来量化静息状态下的全脑拓扑特性，包括聚类系数、全局效率、参与系数和模块度。对于每个度量标准，我们计算了所有脑区的平均值，并在不同的网络密度下迭代此过程。比较了两组之间的网络密度与每个网络度量标准的曲线。最后，我们通过结合全脑平均聚类系数和全局效率曲线，并应用基于主成分分析（PCA）的降维方法，得出了一个综合度量标准，我们称之为“整合-分离轴”。
与 UTLE 相比，BiTLE 的全局效率降低（p=0.031），在一系列网络密度下全脑平均参与系数降低（p=0.019）。最大模块化产生了更多的小社区，BiTLE 中的小社区数量多于 UTLE（p=0.020）。网络特性的差异沿着整合-分离轴将 BiTLE 和 UTLE 分开，轴内区域对 BiTLE 的特异性高达 0.87。沿着整合-分离轴，手术效果差的 UTLE 患者分布在与 BiTLE 相同的区域，网络度量标准证实了 BiTLE 和手术效果差的 UTLE 中存在类似的分离增加模式。
通过 rs-fMRI 测量的发作间期全脑网络分离增加，不仅对 BiTLE 具有特异性，而且对手术效果差的 UTLE 也具有特异性，这可能有助于在颅内脑电图或设备植入之前，非侵入性地识别出这一患者群体。
