将诊断与疾病表征分离：神经动力学引导变形的双视角脑电学习
Separating Diagnosis from Disease Representation: Dual-View EEG Learning with Neural-Dynamics-Guided Deformation
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中文总结 AI 辅助
针对脑电神经调控需结构化状态的问题，提出DMD-EEG双视角学习，分离诊断与疾病表征，以低秩稀疏变形建模源空间，在多种疾病中验证有效性并实现跨导联迁移。
中文摘要 AI 辅助
基于脑电图（EEG）的闭环神经调控需要一种针对特定主体的结构化状态，而非单一疾病概率，用以指明哪些脑区在哪些频率和哪些滞后上出现异常。传感器空间模型保留了最强的诊断证据但缺乏解剖信息，源空间模型提供了解剖信息却损失了预测信号，事后归因则停留在预测之外。我们不将两者强行融合为单一表征，而是将其分离，并提出DMD-EEG（脑电双视角多尺度变形），该方法保留固定的头皮频谱专家用于诊断，并将源空间疾病相关表征建模为健康神经动力学先验在46个感兴趣区域（ROI）×5个频率×4个滞后（自相关时间尺度）空间中的低秩、稀疏、迭代变形。两个专家仅在固定的决策层面汇合，因此源状态在架构上与头皮专家分离。在重度抑郁症（MDD）、首发精神病（FEP）和帕金森病（PD）中，决策级融合在MDD和FEP上匹配最强单一专家，并在PD上超过源分支。在FEP上，源专家是最强分支，也是变形贡献最大的任务。源状态是在跨导联布局的共享源坐标系中定义的显式ROI-频率-滞后归因，我们将其视为一种解剖协调的预测表征，其坐标可直接读取并产生假设。最高显著性坐标与已确立的疾病回路一致（MDD中的额-边缘-颞区，PD中的运动皮层β频段），且MDD状态按秩迁移到使用不同导联布局记录的未见队列。
英文摘要
Electroencephalography (EEG)-based closed-loop neuromodulation calls for a subject-specific structured state, as opposed to a single disease probability, specifying which brain regions are deviant, at which frequencies, and at which lags. Sensor-space models keep the strongest diagnostic evidence without anatomy, source-space models give anatomy at a loss of predictive signal, and post-hoc attributions stay outside the prediction. We separate the two instead of forcing them into one representation, and propose DMD-EEG (Dual-view Multiscale Deformation for EEG), which keeps a fixed scalp spectral expert for diagnosis and models the source-space disease-related representation as a low-rank, sparse, iterative deformation of a healthy neural-dynamics prior in a $46$-region-of-interest (ROI) $\times$ $5$-frequency $\times$ $4$-lag (autocorrelation-timescale) space. The two experts meet only at a fixed decision level, so the source state is architecturally separate from the scalp expert. Across major depressive disorder (MDD), first-episode psychosis (FEP), and Parkinson's disease (PD), decision-level fusion matches the strongest single expert on MDD and FEP and exceeds the source branch on PD. On FEP the source expert is the strongest branch, the task where the deformation contributes most. The source state is an explicit ROI-frequency-lag attribution defined in a shared source coordinate system across montages, which we treat as an anatomically-coordinated predictive representation whose coordinates are directly readable and hypothesis-generating. The highest-saliency coordinates align with established disease circuitry (fronto-limbic-temporal regions in MDD, motor-cortex beta in PD), and the MDD state transfers by rank to an unseen cohort recorded with a different montage.
