Table_4_Automated seizure onset zone locator from resting-state functional MRI in drug-resistant epilepsy.docx

ObjectiveAccurate localization of a seizure onset zone (SOZ) from independent components (IC) of resting-state functional magnetic resonance imaging (rs-fMRI) improves surgical outcomes in children with drug-resistant epilepsy (DRE). Automated IC sorting has limited success in identifying SOZ localizing ICs in adult normal rs-fMRI or uncategorized epilepsy. Children face unique challenges due to the developing brain and its associated surgical risks. This study proposes a novel SOZ localization algorithm (EPIK) for children with DRE.MethodsEPIK is developed in a phased approach, where fMRI noise-related biomarkers are used through high-fidelity image processing techniques to eliminate noise ICs. Then, the SOZ markers are used through a maximum likelihood-based classifier to determine SOZ localizing ICs. The performance of EPIK was evaluated on a unique pediatric DRE dataset (n = 52). A total of 24 children underwent surgical resection or ablation of an rs-fMRI identified SOZ, concurrently evaluated with an EEG and anatomical MRI. Two state-of-art techniques were used for comparison: (a) least squares support-vector machine and (b) convolutional neural networks. The performance was benchmarked against expert IC sorting and Engel outcomes for surgical SOZ resection or ablation. The analysis was stratified across age and sex.ResultsEPIK outperformed state-of-art techniques for SOZ localizing IC identification with a mean accuracy of 84.7% (4% higher), a precision of 74.1% (22% higher), a specificity of 81.9% (3.2% higher), and a sensitivity of 88.6% (16.5% higher). EPIK showed consistent performance across age and sex with the best performance in those < 5 years of age. It helped achieve a ~5-fold reduction in the number of ICs to be potentially analyzed during pre-surgical screening.SignificanceAutomated SOZ localization from rs-fMRI, validated against surgical outcomes, indicates the potential for clinical feasibility. It eliminates the need for expert sorting, outperforms prior automated methods, and is consistent across age and sex.

旨在从静息态功能性磁共振成像（rs-fMRI）的独立成分（IC）中精确定位癫痫发作起始区（SOZ），有助于改善药物难治性癫痫（DRE）儿童的外科手术效果。在成人正常rs-fMRI或未分类癫痫的独立成分自动分类中，自动IC分类在识别SOZ定位IC方面成功有限。由于大脑发育及其相关的手术风险，儿童面临着独特的挑战。本研究提出了一种针对DRE儿童的新型SOZ定位算法（EPIK）。方法上，EPIK采用分阶段的方法开发，通过高保真图像处理技术利用与fMRI噪声相关的生物标志物来消除噪声IC。然后，通过基于最大似然率的分类器使用SOZ标志来确定SOZ定位IC。EPIK的性能在一个独特的儿童DRE数据集（n=52）上进行了评估。共有24名儿童接受了rs-fMRI识别的SOZ的外科切除或消融手术，同时进行了脑电图和解剖MRI的评估。比较了两种最先进的 技术：(a) 最小二乘支持向量机 和 (b) 卷积神经网络。性能与专家IC分类和Engel手术SOZ切除或消融的结果进行了基准测试。分析按年龄和性别分层进行。结果EPIK在SOZ定位IC识别方面优于最先进的技术，平均准确率达到84.7%（高出4%），精确率为74.1%（高出22%），特异度为81.9%（高出3.2%），灵敏度达到88.6%（高出16.5%）。EPIK在年龄和性别上表现出一致的性能，在5岁以下儿童中表现最佳。它帮助将术前筛查中可能需要分析的IC数量减少了约5倍。意义从rs-fMRI自动定位SOZ，并与手术结果进行验证，表明了临床可行性。它消除了对专家分类的需求，优于先前的自动方法，并且在年龄和性别上保持一致。