从间接神经影像观测中发现潜在空间因果关系
Latent-Space Causal Discovery from Indirect Neuroimaging Observations
AI总结：
本文提出INCAMA方法，通过物理感知逆向与延迟感知Mamba编码器，提升从间接神经影像中恢复因果结构的性能，实验显示在TVB模拟和HCP任务fMRI中表现更优。
摘要：
神经影像不直接观测因果变量：血流动力学和体积传导会扭曲信号，使得统计依赖不反映潜在神经影响。在估计图之前，必须明确在何种假设下可以从此类间接观测中研究延迟的定向结构。我们正式化一个条件设置——在模态物理和非平稳潜在动态下可恢复的逆向，并在显式假设下推导出逆向误差传播界。基于此框架，我们提出INCAMA（INdirect CAusal MAmba）：物理感知逆向与延迟感知Mamba编码器，利用机制位移作为信息丰富的变化来评分定向图。我们使用受控模拟进行定量验证，并使用HCP运动任务fMRI作为零样本外部迁移检查，基于解剖学和任务网络一致性。在TVB模拟中，INCAMA在F1指标上比观测空间和两阶段基线提高了2-3倍，在HCP运动任务fMRI中产生稀疏的定向估计，集中在经典视觉-运动通路中。
英文摘要：
Neuroimaging does not observe causal variables directly: hemodynamics and volume conduction distort signals so that statistical dependence need not reflect latent neural influence. Before estimating graphs, one must specify under what assumptions delayed directed structure can be studied from such indirect observations. We formalize a conditional setting - recoverable inversion under modality physics together with nonstationary latent dynamics - and derive an inversion-error propagation bound under explicit assumptions. Building on this framing, we propose INCAMA (INdirect CAusal MAmba): physics-aware inversion coupled with a delay-aware Mamba encoder that uses mechanism shifts as informative variation for directed graph scoring. We use controlled simulations for quantitative validation and HCP motor-task fMRI as a zero-shot external transfer check based on anatomical and task-network consistency. Across TVB simulations, INCAMA improves directed-structure recovery by 2-3x in F1 over observation-space and two-stage baselines, and on HCP motor-task fMRI it produces sparse directed estimates concentrated in canonical visuo-motor pathways.
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