Recent deep learning methods for fMRI-based diagnosis have achieved promising accuracy by modeling functional connectivity networks. However, standard approaches often struggle with noisy interactions, and conventional post-hoc attribution methods may lack reliability, potentially highlighting dataset-specific artifacts. To address these challenges, we introduce PIME, an interpretable framework that bridges intrinsic interpretability with minimal-sufficient subgraph optimization by integrating prototype-based classification and consistency training with structural perturbations during learning. This encourages a structured latent space and enables Monte Carlo Tree Search (MCTS) under a prototype-consistent objective to extract compact minimal-sufficient explanatory subgraphs post-training. Experiments on three benchmark fMRI datasets demonstrate that PIME achieves state-of-the-art performance. Furthermore, by constraining the search space via learned prototypes, PIME identifies critical brain regions that are consistent with established neuroimaging findings. Stability analysis shows 90% reproducibility and consistent explanations across atlases.

翻译：基于功能磁共振成像的诊断方法通过建模功能连接网络，近期已取得令人瞩目的准确率。然而，标准方法常受噪声交互影响，且传统的事后归因方法可能缺乏可靠性，易凸显数据集特异性伪影。为应对这些挑战，我们提出PIME——一个可解释框架，该框架通过学习过程中整合基于原型的分类与结构扰动下的一致性训练，将内在可解释性与最小充分子图优化相连接。这促使形成结构化的潜在空间，并使得在原型一致性目标下进行蒙特卡洛树搜索（MCTS）以在训练后提取紧凑的最小充分解释子图成为可能。在三个基准功能磁共振成像数据集上的实验表明，PIME实现了最先进的性能。此外，通过借助学习到的原型约束搜索空间，PIME识别出的关键脑区与既有的神经影像学发现相一致。稳定性分析显示，其在不同脑图谱间具有90%的可重复性及一致的解释结果。