NeuronSifter: Intervention Planning in CNS Microenvironments

2026-09-28

AI Summary

This study addresses the problem of inefficient decision-making in central nervous system interventions caused by neglecting spatiotemporal target occupancy and joint uncertainty. To this end, we propose a novel intervention decision framework that compiles dosing regimens into state-conditioned target occupancy fields and introduces an occupancy-conditioned diffusion operator to propagate microenvironmental dynamics. Furthermore, it employs an expected loss reduction criterion for decision-oriented measurement acquisition and posterior assimilation, thereby fully preserving joint uncertainty. The core contribution lies in reframing decision quality as an intrinsic property of the intervention interface. In synthetic Alzheimer’s disease evaluations, the proposed approach reduces trajectory CRPS to 0.110 and achieves an intervention ranking accuracy of 0.880, enabling superior terminal risk control at reduced cost.

Abstract

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.

Problem

• CNS intervention planning
• microenvironment dynamics
• target occupancy
• decision uncertainty
• measurement selection

Innovation

• target-occupancy fields
• occupancy-conditioned diffusion operator
• decision-directed acquisition
• intervention planning
• CNS microenvironments