发表机构
Keio University（庆应义塾大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对电生理源成像不适定问题，提出CANDLE模型，利用零空间先验在受试者特定皮层几何上估计源活动，基于大规模模拟器训练，优于现有方法并泛化到两个临床任务。
AI 中文摘要
电生理源成像（ESI）旨在从脑电图（EEG）等非侵入性电生理测量中估计皮层源活动。然而，ESI 本质上是不适定的，因为源活动的维度远高于传感器观测，导致解不唯一。近期基于学习的方法通过学习数据驱动的源先验来解决这一歧义，但它们在跨受试者特定皮层几何结构的泛化上往往存在困难。为解决此问题，我们提出 CANDLE，一种基于学习的 ESI 模型，可在受试者特定皮层几何结构上估计源活动。CANDLE 学习由 T1 加权 MRI 导出的源到传感器映射所诱导的零空间上的先验，将学习限制在不可观测的源成分上，同时保留几何约束。为训练 CANDLE，我们开发了一个全脑模拟器，涵盖超过 1,100 个受试者特定皮层几何结构，源配置来自超过 26,000 个统计脑图。仅使用模拟数据训练的 CANDLE 在模拟源活动估计上优于先前的 ESI 方法，并泛化到两个实证任务：（i）从同步记录的头皮 EEG 进行颅内刺激定位；（ii）从术前发作间期 EEG 进行致痫区估计。我们的项目页面可在 https URL 获取。
英文摘要
Electrophysiological source imaging (ESI) aims to estimate cortical source activity from noninvasive electrophysiological measurements such as electroencephalogram (EEG). However, ESI is fundamentally ill-posed because source activity is substantially higher-dimensional than sensor observations, resulting in non-unique solutions. Recent learning-based approaches address this ambiguity by learning data-driven source priors, yet they often struggle to generalize across subject-specific cortical geometries. To address this, we propose CANDLE, a learning-based ESI model that estimates source activity on subject-specific cortical geometries. CANDLE learns a prior over the null space induced by the source-to-sensor mapping derived from T1-weighted MRI, restricting learning to unobservable source components while preserving geometric constraints. To train CANDLE, we develop a whole-brain simulator spanning over 1,100 subject-specific cortical geometries with source configurations derived from over 26,000 statistical brain maps. Trained exclusively on simulated data, CANDLE outperformed prior ESI methods on simulated source activity estimation and generalized to two empirical tasks: (i) intracranial stimulation localization from simultaneously recorded scalp EEG and (ii) epileptogenic zone estimation from presurgical interictal EEG. Our project page is available at https://candle-esi.pages.dev}{https://candle-esi.pages.dev.
