Davoodi-Bojd Esmaeil, Elisevich Kost V, Schwalb Jason, Air Ellen, Soltanian-Zadeh Hamid
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:1103-1106. doi: 10.1109/EMBC.2016.7590896.
A prerequisite of temporal lobe epilepsy (TLE) surgery is to lateralize the disease. Recent studies have shown the capability of diffusion weighted MRI (DWMRI) in lateralizing TLE patients. This has been achieved by analyzing diffusion parameters of specific white matter tracts or regions known to be involved in the disease; however, other brain regions and connections have not been investigated for TLE lateralization. Whole brain structural connectivity using DWMRI provides a wealth of information regarding the structural connections in the brain. This information can be explored to find the most effective connections for TLE lateralization. In this work, we investigate the connectivity matrices calculated from DWMRI of 10 left and 10 right TLE patients to find the most effective connections for lateralizing the disease. Linear support vector machine (LSVM) classifier and leave-one-out cross validation scheme are used to estimate classification performance of the connectivity feature subsets. A subset of three connections with 100% classification accuracy is found. The corresponding LSVM classifier may be used to lateralize prospective TLE patients.
颞叶癫痫(TLE)手术的一个前提条件是确定疾病的侧别。最近的研究表明,扩散加权磁共振成像(DWMRI)有能力对TLE患者进行侧别判断。这是通过分析已知与该疾病相关的特定白质束或区域的扩散参数来实现的;然而,尚未针对TLE侧别对其他脑区和连接进行研究。使用DWMRI的全脑结构连接性提供了大量有关大脑结构连接的信息。可以利用这些信息来找到用于TLE侧别的最有效连接。在这项工作中,我们研究了从10例左侧TLE患者和10例右侧TLE患者的DWMRI计算得出的连接矩阵,以找到用于疾病侧别的最有效连接。使用线性支持向量机(LSVM)分类器和留一法交叉验证方案来估计连接特征子集的分类性能。发现了一个具有100%分类准确率的由三个连接组成的子集。相应的LSVM分类器可用于对未来的TLE患者进行侧别判断。