从未知局部干预中识别神经源动力学
Identifying Neural Source Dynamics from Unknown Local Interventions
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中文总结 AI 辅助
本文证明未知局部机制变化可为脑电图源动力学识别提供缺失信息，提出基于秩一特征和正演模型的直接估计器，在模拟中成功恢复全部动力学。
中文摘要 AI 辅助
脑电图（EEG）记录的是脑源活动的混合信号。即使已知解剖学正演模型，仅激发源状态空间一部分的实验仍会使动力学无法被识别，重复实验也无法消除这种模糊性。我们证明，未知的局部机制变化可以提供缺失的信息。我们考虑固定解剖源之间的线性动力学，并假设源状态初始化模式已知。改变一个源在单次转移中的更新规则，会在后续脑电图中留下一个秩为一的、源特异的特征：减去匹配的基线响应可将其分离出来，正演模型可识别该源并校准其响应历史。将这些历史与初始化响应相结合，可在无需基线可达性且无需先识别干预系数的情况下恢复源间相互作用。我们建立了充分的恢复条件、直接估计器以及以正确源标签为条件的噪声敏感性界。基于磁共振成像解剖结构的模拟脑电图证实了信息增益：当基线激发仅限于十二个源坐标中的四个时，八个未知变化可在32/32个系统中恢复全部动力学，而基线实现、通过可逆正演模型的基线回归以及使测试状态未被暴露的变化均失败，且显式构造的替代动力学可重现每个基线均值。在基线信息充分的情况下，直接重建也比匹配信息的谱估计器更可靠。即使源标签正确，非局部变化和正演模型误差也会限制准确性。
英文摘要
Electroencephalography (EEG) records mixtures of brain-source activity. Even with a known anatomical forward model, experiments that excite only part of the source-state space leave the dynamics unidentified, and repetition cannot resolve the ambiguity. We show that unknown local mechanism changes can supply the missing information. We consider linear dynamics among fixed anatomical sources with known source-state initialization patterns. Changing one source's update rule for one transition leaves a rank-one, source-specific signature in subsequent EEG: subtracting matched baseline responses isolates it, and the forward model identifies the source and calibrates its response history. Combining these histories with initialization responses recovers source interactions without baseline reachability and without first identifying the intervention coefficients. We establish sufficient recovery conditions, a direct estimator, and a noise-sensitivity bound conditional on correct source labels. Simulated EEG on anatomy derived from magnetic resonance imaging confirms the information gain: with baseline excitation confined to four of twelve source coordinates, eight unknown changes recover all dynamics in 32/32 systems, whereas baseline realization, baseline regression through an invertible forward model, and changes that leave the tested states unexposed all fail, and explicitly constructed alternative dynamics reproduce every baseline mean. Where baseline information suffices, direct reconstruction is also more reliable than a matched-information spectral estimator. Nonlocal changes and forward-model error limit accuracy even when source labels are correct.
发表机构
- Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）
- Carnegie Mellon University (CMU)（卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。
