信息瓶颈引导的异构图学习用于可解释性神经发育障碍诊断
Information Bottleneck-Guided Heterogeneous Graph Learning for Interpretable Neurodevelopmental Disorder Diagnosis
- Department of Language Science and Technology, The Hong Kong Polytechnic University（香港理工大学语言科学与技术系）
- Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University（上海 Maritime 大学数字图像与智能计算实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出I2B-HGNN框架，结合信息瓶颈原理指导脑连接建模与跨模态特征融合，实现高分类准确率和可解释性生物标志物识别。
AI中文摘要：
开发用于神经发育障碍（NDDs）诊断的可解释模型面临有效编码、解码和整合多模态神经影像数据的重大挑战。尽管许多现有机器学习方法在脑网络分析中表现出色，但通常存在解释性有限的问题，特别是在从功能性磁共振成像（fMRI）数据中提取有意义的生物标志物以及建立影像特征与人口统计学特征之间明确关系方面。此外，当前图神经网络方法在捕捉局部和全局功能连接模式的同时实现理论上合理的多模态数据融合方面存在局限。为此，我们提出了可解释信息瓶颈异构图神经网络（I2B-HGNN），这是一个统一框架，应用信息瓶颈原理指导脑连接建模和跨模态特征整合。该框架包含两个互补组件。第一个是信息瓶颈图Transformer（IBGraphFormer），它通过信息瓶颈引导的池化结合基于Transformer的全局注意力机制和图神经网络，以识别充分的生物标志物。第二个是信息瓶颈异构图注意力网络（IB-HGAN），它采用元路径为基础的异构图学习与结构一致性约束，以实现神经影像和人口统计学数据的可解释融合。实验结果表明，I2B-HGNN在诊断NDDs方面表现出色，具有高分类准确率和提供可解释性生物标志物识别的能力，同时有效分析非影像数据。
英文摘要：
Developing interpretable models for neurodevelopmental disorders (NDDs) diagnosis presents significant challenges in effectively encoding, decoding, and integrating multimodal neuroimaging data. While many existing machine learning approaches have shown promise in brain network analysis, they typically suffer from limited interpretability, particularly in extracting meaningful biomarkers from functional magnetic resonance imaging (fMRI) data and establishing clear relationships between imaging features and demographic characteristics. Besides, current graph neural network methodologies face limitations in capturing both local and global functional connectivity patterns while simultaneously achieving theoretically principled multimodal data fusion. To address these challenges, we propose the Interpretable Information Bottleneck Heterogeneous Graph Neural Network (I2B-HGNN), a unified framework that applies information bottleneck principles to guide both brain connectivity modeling and cross-modal feature integration. This framework comprises two complementary components. The first is the Information Bottleneck Graph Transformer (IBGraphFormer), which combines transformer-based global attention mechanisms with graph neural networks through information bottleneck-guided pooling to identify sufficient biomarkers. The second is the Information Bottleneck Heterogeneous Graph Attention Network (IB-HGAN), which employs meta-path-based heterogeneous graph learning with structural consistency constraints to achieve interpretable fusion of neuroimaging and demographic data. The experimental results demonstrate that I2B-HGNN achieves superior performance in diagnosing NDDs, exhibiting both high classification accuracy and the ability to provide interpretable biomarker identification while effectively analyzing non-imaging data.
