tSymPerturb将纵向症状网络转换为时间索引的干预策略
tSymPerturb converts longitudinal symptom networks into time-indexed intervention strategies
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中文总结 AI 辅助
tSymPerturb是针对交叉滞后面板网络的SymPerturb时间扩展框架，可将纵向症状网络转换为时间索引的干预假设，经多组模拟验证了其有效性。
中文摘要 AI 辅助
纵向症状网络编码了不同测量时点间的定向预测关系，但仅靠出度连接性无法确定应修改哪个症状、修改强度，也无法确定扰动如何向后续症状传播。我们提出tSymPerturb，这是针对交叉滞后面板网络（CLPNs）的SymPerturb的时间扩展框架。该框架将源状态算子（时间虚拟敲除与敲低）、转移算子（定向边与源节点通信阻断）以及策略流程（剂量扰动、组合分析与序列优化）相分离。对于两波线性CLPN，核心传播恒等式为Δμ₂ = B(μ₁ - μ₁*)，该式明确了源时点、结果时点与转移算子。该公式还产生三个证伪约束：在固定线性转移模型与线性剂量映射下，剂量反应完全呈线性；独立源状态扰动在均值层面可加；且无法从单一两波转移中识别真实治疗顺序。在已知的22节点、4模块生成系统中，分析性时间敲除响应与25万次抽样的蒙特卡洛估计值在0.0057个标准差内一致。在200个独立生成的数据集上，与总体tVPPS排名的中位数斯皮尔曼相关系数从n=250时的0.76升至n=500时的0.88、n=1000时的0.93；中位数前5名恢复率分别为0.60、0.80和0.80。多波模拟显示，尽管整体排名一致性较高，但目标特征会随传播范围变化。因此，tSymPerturb在保留预测与因果治疗效应区分的同时，将纵向网络结构转换为可审计的时间索引干预假设。
英文摘要
Longitudinal symptom networks encode directed prediction across measurement occasions, but outgoing connectivity does not by itself identify which symptom should be modified, how strongly it should be changed, or how a perturbation would propagate to later symptoms. We introduce tSymPerturb, a temporal extension of SymPerturb for cross-lagged panel networks (CLPNs). The framework separates source-state operators (temporal virtual knockout and knockdown), transition operators (directed edge and source-node communication blocking), and strategy procedures (dosage perturbation, combination analysis and sequence optimisation). For a two-wave linear CLPN, the central propagation identity is $Δμ_2 = B(μ_1 - μ_1^*)$, which makes the source time, outcome time and transition operator explicit. The formulation also yields three falsification constraints: dose response is exactly linear under a fixed linear transition model and linear dose map; independent source-state perturbations are additive at the mean level; and genuine treatment order is not identified from a single two-wave transition. In a known 22-node, four-module generating system, analytical temporal-knockout responses agreed with 250,000-draw Monte Carlo estimates within 0.0057 standard deviations. Across 200 independently generated datasets, median Spearman correlation with the population tVPPS ranking increased from 0.76 at n=250 to 0.88 at n=500 and 0.93 at n=1,000; median top-five recovery was 0.60, 0.80 and 0.80, respectively. Multi-wave simulations showed that target profiles can change across propagation horizons despite high overall rank concordance. tSymPerturb therefore converts longitudinal network structure into auditable, time-indexed intervention hypotheses while retaining the distinction between prediction and causal treatment effects.
