Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada; Institute of Cyclotron and Drug Discovery Research, Southern TOHOKU Research Institute for Neuroscience, 7- 61-2, Yatsumada, Koriyama, 963-8052, Japan.
Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, 4-1-1, Ogawahigashi-cho, Kodaira, Tokyo, 187-8551, Japan; Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London WC1N 3BG, United Kingdom.
Epilepsy Res. 2020 Nov;167:106474. doi: 10.1016/j.eplepsyres.2020.106474. Epub 2020 Sep 22.
In this paper, we assessed the reliability of glucose metabolic brain data for identifying lateralization of magnetic resonance imaging (MRI)-negative temporal lobe epilepsy (TLE) patients. We designed and developed an efficacious and automatic metabolic-wise lateralization framework. The proposed lateralization framework comprises three main systematic levels. In the first stage of our investigation, we pre-processed interictal fluorodeoxyglucose positron emission tomography images to extract glucose metabolic brain data. In the second stage, we used a voxel selection method involving a feature-ranking strategy to select the most discriminative metabolic voxels. Finally, we used a support vector machine followed by a 10-fold cross-validation strategy to assess the proposed lateralization framework in 27 patients with right MRI-negative TLE and 29 patients with left MRI-negative TLE. The proposed lateralization framework achieved an excellent accuracy of 96.43 % concordance with experienced PET interpreter. Thus, we show that pattern analysis of glucose metabolic brain data can accurately lateralize MRI-negative TLE patients in the clinical setting.
在本文中，我们评估了葡萄糖代谢脑数据在识别磁共振成像 (MRI) 阴性颞叶癫痫 (TLE) 患者侧化中的可靠性。我们设计并开发了一种有效且自动的代谢侧化框架。所提出的侧化框架包括三个主要系统级别。在我们研究的第一阶段，我们对发作间期氟脱氧葡萄糖正电子发射断层扫描图像进行预处理，以提取葡萄糖代谢脑数据。在第二阶段，我们使用了一种涉及特征排序策略的体素选择方法来选择最具区分性的代谢体素。最后，我们使用支持向量机和 10 折交叉验证策略来评估 27 例右侧 MRI 阴性 TLE 患者和 29 例左侧 MRI 阴性 TLE 患者的拟议侧化框架。所提出的侧化框架与有经验的 PET 解释器具有 96.43%的一致性准确率。因此，我们表明，葡萄糖代谢脑数据的模式分析可以在临床环境中准确地对 MRI 阴性 TLE 患者进行侧化。
