Surgical disconnection of Seizure Onset Zones (SOZs) at an early age is an effective treatment for Pharmaco-Resistant Epilepsy (PRE). Pre-surgical localization of SOZs with intra-cranial EEG (iEEG) requires safe and effective depth electrode placement. Resting-state functional Magnetic Resonance Imaging (rs-fMRI) combined with signal decoupling using independent component (IC) analysis has shown promising SOZ localization capability that guides iEEG lead placement. However, SOZ ICs identification requires manual expert sorting of 100s of ICs per patient by the surgical team which limits the reproducibility and availability of this pre-surgical screening. Automated approaches for SOZ IC identification using rs-fMRI may use deep learning (DL) that encodes intricacies of brain networks from scarcely available pediatric data but has low precision, or shallow learning (SL) expert rule-based inference approaches that are incapable of encoding the full spectrum of spatial features. This paper proposes DeepXSOZ that exploits the synergy between DL based spatial feature and SL based expert knowledge encoding to overcome performance drawbacks of these strategies applied in isolation. DeepXSOZ is an expert-in-the-loop IC sorting technique that a) can be configured to either significantly reduce expert sorting workload or operate with high sensitivity based on expertise of the surgical team and b) can potentially enable the usage of rs-fMRI as a low cost outpatient pre-surgical screening tool. Comparison with state-of-art on 52 children with PRE shows that DeepXSOZ achieves sensitivity of 89.79%, precision of 93.6% and accuracy of 84.6%, and reduces sorting effort by 6.7-fold. Knowledge level ablation studies show a pathway towards maximizing patient outcomes while optimizing the machine-expert collaboration for various scenarios.

翻译：早期手术离断致痫区（SOZ）是治疗药物难治性癫痫（PRE）的有效手段。术前通过颅内脑电图（iEEG）定位SOZ需要安全、有效的深部电极植入。结合独立成分（IC）分析进行信号解耦的静息态功能磁共振成像（rs-fMRI）已展现出引导iEEG电极置入的潜在SOZ定位能力。然而，SOZ对应的IC识别需要手术团队对每位患者的数百个IC进行人工专家筛选，这限制了该术前筛查手段的可重复性和普及性。利用rs-fMRI实现SOZ IC自动识别的方法，可采用深度学习（DL）从稀缺的儿科数据中编码脑网络的复杂特征，但精度较低；或采用基于浅层学习（SL）专家规则推理的方法，但此类方法无法编码空间特征的完整谱系。本文提出DeepXSOZ，通过融合基于DL的空间特征与基于SL的专家知识编码，克服单独应用以上策略的性能缺陷。DeepXSOZ是一种专家参与式IC排序技术，其特点包括：a)可配置为显著降低专家筛选工作量，或根据手术团队的专业水平保持高灵敏度运行；b)有望使rs-fMRI成为低成本门诊术前筛查工具。与52例PRE患儿的现有最优方法对比显示，DeepXSOZ灵敏度达89.79%，精确率达93.6%，准确率达84.6%，并将筛选工作量降低6.7倍。知识层级消融研究揭示了在多种场景下，通过优化人机协作实现患者预后最大化的路径。