基于立体脑电图记录的致痫灶定位图学习方法
Graph-Based Approaches to Learning Epileptogenic Zone Localization Using Stereo-EEG Recordings
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中文总结 AI 辅助
该研究针对40例患者，对比不同图拓扑的图学习模型，发现Region-Bridge-$c$拓扑在致痫灶定位中表现最优，证明图构建需明确评估。
中文摘要 AI 辅助
致痫灶（EZ）是个体大脑中产生癫痫发作的区域，是癫痫手术的目标。从立体脑电图（sEEG）记录中定位致痫灶可支持手术规划，但人工解读耗时且聚焦于发作期记录。利用记录脑区间静息态功能连接的图学习模型是一种有吸引力的替代方案，但关键取决于模型选择的网络拓扑结构。我们开展了一项受控图模型研究，以探索图拓扑如何影响40例患者静息态sEEG的致痫灶定位。采用相同的简单可学习模型和留一患者交叉验证，我们对比了稠密图、基于解剖学和几何学的先验、预算稀疏化方法及学习型稀疏化，包括提出的Region-Bridge-$c$拓扑。为公平对比图构建，我们控制每个节点的入边数量并调整图稀疏度。在约30%边保留率下，Region-Bridge-$c$取得了观测到的最高平均PR-AUC（0.371±0.015；ROC-AUC 0.743±0.010），同时比稠密图少用约69%的边（稠密图PR-AUC为0.349±0.014）。Spatial-$k$具有竞争力，而随机剪枝需接近稠密的边保留率。学习型稀疏化受益于解剖学节点元数据，但平均而言未超越最佳固定先验。所有拓扑中，最佳选择随患者而异。这些结果表明，图构建应明确评估，而非视为固定预处理步骤。
英文摘要
The epileptogenic zone (EZ) is the brain region that generates seizures in an individual, and is the target of epilepsy surgery. Localizing the EZ from stereo-EEG (sEEG) recordings supports surgical planning, but manual interpretation is time-consuming and focuses on seizure recordings. Graphical learning models of resting-state functional connectivity among the recorded brain regions are an attractive alternative, but depend crucially on the network topology chosen for the model. We present a controlled study of graph-based models to explore how graph topology affects EZ localization from resting-state sEEG in 40 patients. Using the same simple learnable model and leave-one-patient-out evaluation, we compare dense graphs, anatomy- and geometry-informed priors, budgeted sparsification methods, and learned sparsification, including the proposed Region-Bridge-$c$ topology. To compare graph constructions fairly, we control the number of incoming edges per node and vary graph sparsity. At $\approx 30\%$ edge retention, Region-Bridge-$c$ achieves the highest observed mean PR-AUC ($0.371\pm0.015$; ROC-AUC $0.743\pm0.010$) while using $\approx 69\%$ fewer edges than Dense (PR-AUC $0.349\pm0.014$). Spatial-$k$ is competitive, whereas random pruning requires near-dense retention. Learned sparsification benefits from anatomical node metadata but, on average, does not surpass the best fixed prior. Across all topologies, the best choice varies by patient. These results suggest that graph construction should be evaluated explicitly rather than treated as fixed preprocessing.
发表机构
- University of Cincinnati（辛辛那提大学）
- Cincinnati Children’s Hospital Medical Center（辛辛那提儿童医院医疗中心）
机构由 AI 辅助整理，请以论文原文为准。
