Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

PIME: Prototype-based Interpretable MCTS-Enhanced Brain Network Analysis for Disorder Diagnosis

Kunyu Zhang, Yanwu Yang, Jing Zhang, Xiangjie Shi, Shujian Yu

TL;DR

PIME is 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, and enables Monte Carlo Tree Search under a prototype-consistent objective to extract compact minimal-sufficient explanatory subgraphs post-training.

Abstract

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.

PIME: Prototype-based Interpretable MCTS-Enhanced Brain Network Analysis for Disorder Diagnosis

TL;DR

PIME is 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, and enables Monte Carlo Tree Search under a prototype-consistent objective to extract compact minimal-sufficient explanatory subgraphs post-training.

Abstract

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.
Paper Structure (12 sections, 9 equations, 2 figures, 4 tables)

This paper contains 12 sections, 9 equations, 2 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. MCTS for Interpretability
  4. Methodology
  5. Information Bottleneck Prototype Learning
  6. Consistency Training and Regularization
  7. Experiments and Results
  8. Datasets and Experimental Settings
  9. Experimental Results and Ablation Study
  10. MCTS-Based Interpretability Analysis
  11. Prototype Activation Patterns and Stability Analysis
  12. Conclusion and Future Work

Figures (2)

  • Figure 1: Overview of the PIME framework. It consists of a prototype-based IB training pipeline with consistency regularization and a post-training MCTS module for explanatory subgraph extraction.
  • Figure 2: Top 10 MCTS-identified critical brain regions for each diagnostic task. (a) ADNI AD vs NC. (b) ADNI MCI vs NC. (c) ADNI AD vs MCI.