NeuronSifter：中枢神经系统微环境中的干预规划

中文摘要 AI 辅助

对中枢神经系统（CNS）干预措施进行优先级排序，需要预测剂量、给药途径和时间表如何作用于部分可观测的微环境，然后选择能够改变决策的测量方式。动作条件预测器将治疗方案简化为身份令牌或标量暴露量，丢弃了靶点被作用的时空信息；将点估计交给独立的规划器，则丢弃了使测量值得进行的联合不确定性。因此，我们将决策质量视为干预界面的属性，而非控制器位置。NeuronSifter将治疗方案编译为带支持掩码的状态条件靶点占用场，通过占用条件扩散算子将其在微环境动力学中传播，并根据干预损失的预期减少量来选择测量，将类型化结果同化到同一后验中。在64个配对场景块的合成阿尔茨海默病（AD）评估中，占用条件将轨迹连续排名概率得分从0.165降至0.110，并将干预排序准确率从0.760提升至0.880，且每个配对基准对比在Holm校正后仍保持显著差异。决策导向采集达到终末风险0.160，而匹配的数值贝叶斯实验设计规划器为0.166，并以早期设计对照成本的0.796 [0.732, 0.873]达到目标风险，而相对于匹配规划器的对应比值为0.963 [0.907, 1.025]，与相等性无显著差异；点状态和依赖消融界面反而将风险提升至0.220和0.199，而全后验外部控制器则完全持平。已发表的AD试验提供了单独的回顾性终点桥接。

英文摘要

Prioritizing central nervous system (CNS) interventions requires predicting how a dose, route, and schedule act on a partially observed microenvironment, then choosing the measurement that would change the decision. Action-conditioned predictors reduce a regimen to an identity token or a scalar exposure, discarding where and when the target is engaged; handing a point estimate to a separate planner then discards the joint uncertainty that makes a measurement worth running. We therefore treat decision quality as a property of the intervention interface, not of controller placement. NeuronSifter compiles regimens into state-conditional target-occupancy fields with support masks, propagates them through microenvironment dynamics with an occupancy-conditioned diffusion operator, and selects measurements by their expected reduction in intervention loss, assimilating typed outcomes into the same posterior. In a declared synthetic Alzheimer's disease (AD) evaluation over 64 paired scenario blocks, occupancy conditioning lowers trajectory continuous ranked probability score from 0.165 to 0.110 and raises intervention ordering accuracy from 0.760 to 0.880, and every paired benchmark contrast remains separated after Holm correction. Decision-directed acquisition attains terminal risk 0.160 against 0.166 for a matched numerical Bayesian experimental design planner, and reaches the target risk at 0.796 $[0.732,0.873]$ of an earlier design control's cost, while the corresponding ratio against the matched planner, 0.963 $[0.907,1.025]$, is not separated from equality; point-state and dependence-ablated interfaces instead raise risk to 0.220 and 0.199, and a full-posterior external controller ties exactly. Published AD trials supply a separate retrospective endpoint bridge.

发表机构

• Institute of Medical Technology, Peking University Health Science Center（北京大学医学部医学技术研究院）
• Beijing Key Laboratory of Intelligent Neuromodulation and Brain Disorder Treatment（北京智能神经调控与脑疾病治疗重点实验室）
• Department of Radiology, Peking University Third Hospital（北京大学第三医院放射科）
• National Biomedical Imaging Center, College of Future Technology, Peking University（北京大学未来技术学院国家生物医学成像中心）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

This work is not complete enough yet