从间接脑成像数据中推断神经因果结构，提升准确性与可解释性。
Latent-Space Causal Discovery from Indirect Neuroimaging Observations
- 结合物理模型与非平稳动态，设计可逆反演框架
- 在模拟和真实数据上实现3倍于基线的因果结构恢复精度
- 适合关注脑网络因果分析的研究者使用
脑成像无法直接观测因果变量：血流动力学和体积传导会扭曲信号，导致统计相关性不反映潜在神经影响。在估计因果图之前，必须明确在何种假设下可从这些间接观测中研究延迟的有向结构。我们形式化了一种条件设定——在模态物理约束下的可恢复反演，结合非平稳潜变量动态，并在明确假设下推导出反演误差传播界。基于此框架，提出INCAMA（INdirect CAusal MAmba）：融合物理感知反演与延迟感知Mamba编码器，利用机制变化作为有向图评分的有用变异。通过受控仿真进行定量验证，并以HCP运动任务fMRI为零样本外部迁移检验，基于解剖与任务网络一致性评估。在TVB模拟中，INCAMA在F1指标上比观测空间与两阶段基线提升2-3倍；在HCP运动任务fMRI数据上，生成稀疏有向估计，集中于经典视动通路。
原文摘要 · Abstract (English)
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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