用AI模型仅需一晚睡眠数据，精准定位难治性癫痫手术区。
Foundation Models for Epileptogenic Zone Identification in Drug-Resistant Epilepsy
- 构建双模型系统：先用海量脑电数据训练信号模型，再结合临床信息生成可解释预测。
- 接触级阳性预测值达0.978，区域级准确率100%，显著优于传统方法。
- 只需一晚数据即可分析，有望缩短侵入式监测时间，适合神经外科与癫痫研究者。
准确定位难治性癫痫的致痫区（EZ）对术后无发作至关重要，但目前无发作率仍低于50%。我们开发了EpiiSLM，一种基于立体脑电图（sEEG）的双基础模型系统：在蒙特利尔神经学研究所医院104,990分钟的sEEG数据上训练信号基础模型，不区分手术结果，且以非癫痫信号为锚点提取EZ生物标志物；再通过语言基础模型融合sEEG输出与多模态临床信息，生成可解释预测。在留一患者外评估中，EpiiSLM接触级阳性预测值（PPV）达0.978，比以发作起始区（SOZ）作为EZ的基线提升15.1%（p < 0.05），区域级准确率达100%；在外部数据集上，接触级PPV为0.857。EpiiSLM仅需一晚间期睡眠数据，提示可大幅缩短侵入式脑电监测时长，改善手术预后。
原文摘要 · Abstract (English)
Accurate identification of the epileptogenic zone (EZ) is essential for seizure freedom after resective surgery in drug-resistant epilepsy, yet seizure freedom rates remain below 50%. We developed EpiiSLM, a dual foundation model system for EZ identification with stereo-electroencephalography (sEEG), by training a signal foundation model on 104,990 minutes of sEEG recordings from the Montreal Neurological Institute & Hospital, while leveraging all recordings regardless of surgical outcome and anchoring EZ biomarker extraction on non-epileptic signals. A language foundation model then integrates sEEG-derived outputs with multimodal clinical information to produce interpretable predictions. Under leave-one-patient-out evaluation, EpiiSLM achieved 0.978 contact-level positive predictive value (PPV), outperforming the seizure onset zone(SOZ)-as-EZ baseline by 15.1% (p < 0.05), and 100% region-level accuracy; on an external dataset, EpiiSLM achieved 0.857 contact-level PPV. EpiiSLM requires only one night of interictal sleep data, suggesting potential to reduce invasive sEEG monitoring duration and improve surgical outcomes.
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