Department of Psychiatry, National Center of Neurology and Psychiatry, Tokyo, Japan.
Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan.
PLoS One. 2019 Feb 15;14(2):e0212494. doi: 10.1371/journal.pone.0212494. eCollection 2019.
The pathophysiology of idiopathic generalized epilepsy (IGE) is still unclear, but graph theory may help to understand it. Here, we examined the graph-theoretical findings of the gray matter network in IGE using anatomical covariance methods.
We recruited 33 patients with IGE and 35 age- and sex-matched healthy controls. Gray matter images were obtained by 3.0-T 3D T1-weighted MRI and were normalized using the voxel-based morphometry tools of Statistical Parametric Mapping 12. The normalized images were subjected to graph-theoretical group comparison using the Graph Analysis Toolbox with two different parcellation schemes. Initially, we used the Automated Anatomical Labeling template, whereas the Hammers Adult atlas was used for the second analysis.
The resilience analyses revealed significantly reduced resilience of the IGE gray matter networks to both random failure and targeted attack. No significant between-group differences were found in global network measures, including the clustering coefficient and characteristic path length. The IGE group showed several changes in regional clustering, including an increase mainly in wide areas of the bilateral frontal lobes. The second analysis with another region of interest (ROI) parcellation generated the same results in resilience and global network measures, but the regional clustering results differed between the two parcellation schemes.
These results may reflect the potentially weak network organization in IGE. Our findings contribute to the accumulation of knowledge on IGE.
特发性全面性癫痫（IGE）的病理生理学仍不清楚，但图论可能有助于理解。在这里，我们使用解剖协方差方法检查了 IGE 中灰质网络的图论发现。
我们招募了 33 名 IGE 患者和 35 名年龄和性别匹配的健康对照者。使用 3.0-T 3D T1 加权 MRI 获得灰质图像，并使用统计参数映射 12 的基于体素的形态测量工具进行归一化。使用 Graph Analysis Toolbox 对归一化图像进行基于图论的组比较，使用两种不同的分区方案。首先，我们使用自动解剖标记模板，而第二次分析使用 Hammers 成人图谱。
弹性分析表明，IGE 灰质网络对随机故障和靶向攻击的弹性明显降低。全局网络指标（包括聚类系数和特征路径长度）在两组之间没有发现显著差异。IGE 组在区域聚类方面发生了一些变化，包括双侧额叶广泛区域的增加。使用另一个感兴趣区域（ROI）分区的第二次分析在弹性和全局网络指标上产生了相同的结果，但两种分区方案的区域聚类结果不同。
这些结果可能反映了 IGE 中潜在的网络组织薄弱。我们的研究结果为 IGE 的研究积累做出了贡献。
