Department of Neurological Surgery, Washington University School of Medicine, St Louis, Missouri, USA.
Department of Neurology, Washington University School of Medicine, St Louis, Missouri, USA.
Epilepsia. 2022 Jun;63(6):1542-1552. doi: 10.1111/epi.17233. Epub 2022 Apr 1.
Localization of focal epilepsy is critical for surgical treatment of refractory seizures. There remains a great need for noninvasive techniques to localize seizures for surgical decision-making. We investigate the use of deep learning using resting state functional magnetic resonance imaging (RS-fMRI) to identify the hemisphere of seizure onset in temporal lobe epilepsy (TLE) patients.
A total of 2132 healthy controls and 32 preoperative TLE patients were studied. All participants underwent structural MRI and RS-fMRI. Healthy control data were used to generate training samples for a three-dimensional convolutional neural network (3DCNN). RS-fMRI was synthetically altered in randomly lateralized regions in the healthy control participants. The model was then trained to classify the hemisphere containing synthetic noise. Finally, the model was tested on TLE patients to assess its performance for detecting biological seizure onset zones, and gradient-weighted class activation mapping (Grad-CAM) identified the strongest predictive regions.
The 3DCNN classified healthy control hemispheres known to contain synthetic noise with 96% accuracy, and TLE hemispheres clinically identified to be seizure onset zones with 90.6% accuracy. Grad-CAM identified a range of temporal, frontal, parietal, and subcortical regions that were strong anatomical predictors of the seizure onset zone, and the resting state networks that colocalized with Grad-CAM results included default mode, medial temporal, and dorsal attention networks. Lastly, in an analysis of a subset of patients with postsurgical outcomes, the 3DCNN leveraged a more focal set of regions to achieve classification in patients with Engel Class >I compared to Engel Class I.
Noninvasive techniques capable of localizing the seizure onset zone could improve presurgical planning in patients with intractable epilepsy. We have demonstrated the ability of deep learning to identify the correct hemisphere of the seizure onset zone in TLE patients using RS-fMRI with high accuracy. This approach represents a novel technique of seizure lateralization that could improve preoperative surgical planning.
局灶性癫痫的定位对于治疗耐药性癫痫的手术至关重要。对于用于手术决策的癫痫发作定位的非侵入性技术仍然存在巨大需求。我们研究了使用静息态功能磁共振成像（RS-fMRI）的深度学习来识别颞叶癫痫（TLE）患者癫痫发作的半球。
共研究了 2132 名健康对照者和 32 名术前 TLE 患者。所有参与者均接受了结构 MRI 和 RS-fMRI 检查。使用健康对照者的数据生成三维卷积神经网络（3DCNN）的训练样本。在健康对照者参与者的随机侧化区域中综合改变 RS-fMRI。然后，对该模型进行训练以分类包含合成噪声的半球。最后，在 TLE 患者中测试该模型，以评估其检测生物性癫痫发作起始区的性能，梯度加权类激活映射（Grad-CAM）确定了最强的预测区域。
3DCNN 以 96%的准确率对已知包含合成噪声的健康对照者的半球进行分类，以 90.6%的准确率对临床确定为癫痫发作起始区的 TLE 半球进行分类。Grad-CAM 确定了一系列颞叶、额叶、顶叶和皮质下区域，这些区域是癫痫发作起始区的强烈解剖学预测因子，与 Grad-CAM 结果共定位的静息状态网络包括默认模式、内侧颞叶和背侧注意网络。最后，在对术后结局的患者亚组的分析中，与 Engel 分级 I 相比，3DCNN 利用更集中的一组区域在 Engel 分级> I 的患者中实现分类。
能够定位癫痫发作起始区的非侵入性技术可以改善耐药性癫痫患者的术前计划。我们已经证明了深度学习在使用 RS-fMRI 以高精度识别 TLE 患者癫痫发作起始区的正确半球的能力。这种方法代表了一种新的癫痫侧化技术，可能会改善术前手术计划。
