发表机构
Emory University; Emory University School of Medicine（埃默里大学; 埃默里大学医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
XAI-Refine提出自动化解释-知识循环，通过整合事后分析、验证文献并转化为可微约束，实现证据引导的脑龄预测模型修正。
AI 中文摘要
脑龄预测模型通常通过预测准确性进行评估，然而仅凭准确的预测并不能证明模型依赖于可复现或具有神经生物学支持的机制。事后解释方法可以揭示这些机制，但现有的工作流程通常止步于诊断，或者要求在模型分析之前指定修正目标。我们提出了XAI-Refine，一种用于基于静息态功能连接进行脑龄预测的自动化解释-知识循环。在每次迭代中，XAI-Refine将多次重复训练运行中的互补事后分析整合为可靠、结构化的模型解释。它将每个可靠解释转化为一个中性的神经生物学问题，检索并验证相关文献，并将验证后的证据编译到同一类型化解释空间中的可接受集合中。修正目标被定义为当前模型解释在适用已验证知识所诱导的可接受集合上的最小投影。然后，该修订后的解释被转化为可微约束，同时保留原始模型变量、测量算子和适用范围。仅当多种子验证确认了目标导向的解释移动、预测性能保持在预设护栏内且非目标解释漂移保持有界时，候选更新才会被提升。基于功能连接的脑龄预测实验评估了预测性能、解释可靠性、文献对齐和目标特定模型修正，展示了一条从事后分析到证据引导模型修正的结构化路径。
英文摘要
Brain-age prediction models are commonly evaluated by predictive accuracy, yet accurate predictions alone do not establish that a model relies on reproducible or neurobiologically supported mechanisms. Post-hoc explanation methods can expose these mechanisms, but existing workflows typically stop at diagnosis or require correction targets to be specified before model analysis. We propose XAI-Refine, an automated explanation-knowledge loop for brain-age prediction from resting-state functional connectivity. At each iteration, XAI-Refine consolidates complementary post-hoc analyses across repeated training runs into reliable, structured model explanations. It converts each reliable explanation into a neutral neurobiological question, retrieves and verifies relevant literature, and compiles the verified evidence into an admissible set in the same typed explanation space. The target for refinement is defined as the minimal projection of the current model explanation onto the admissible set induced by applicable verified knowledge. This revised explanation is then translated into a differentiable constraint while preserving the originating model variable, measurement operator, and applicable scope. Candidate updates are promoted only when multi-seed validation confirms target-directed explanatory movement, predictive performance remains within a prespecified guardrail, and non-target explanatory drift remains bounded. Experiments on functional-connectivity-based brain-age prediction evaluate predictive performance, explanation reliability, literature alignment, and target-specific model revision, illustrating a structured route from post-hoc analysis to evidence-guided model refinement.
