基于fMRI的疾病诊断的可解释性信息分解脑图学习
Interpretable Information-Decomposed Brain Graph Learning for fMRI-based Disease Diagnosis
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中文总结 AI 辅助
该研究针对fMRI疾病诊断中传统方法的不足，提出可解释图学习框架IID-GCN，分解rs-fMRI交互为三类信息图，整合后在三个数据集上捕捉到更优诊断信息，揭示脑疾病对功能信息组织的重塑作用。
中文摘要 AI 辅助
静息态功能磁共振成像（rs-fMRI）已为计算机辅助诊断实现了功能脑交互的非侵入性映射，但现有多数方法将脑区间关系简化为基于相关性的边权重。这类表征捕捉共波动强度，却模糊了信息在脑区间的共享方式。由于脑疾病可能不仅破坏连接强度，还会破坏冗余性、独特性和协同性的组织，传统功能连接可能遗漏疾病相关的信息结构。本文提出IID-GCN，一种可解释的图学习框架，利用偏熵分解将rs-fMRI交互分解为冗余性、独特性和协同性图。这些信息特异性图分别表征脑活动的共享、区域特异性及联合涌现成分。多通道图卷积网络随后通过边重校准、跨信息交互、ROI注意力读出和通道注意力融合整合分解后的图。在三个数据集上，IID-GCN始终捕捉到超越传统功能连接的互补诊断信息。学习到的信息谱揭示了疾病特异性的冗余性、独特性和协同性改变模式，表明脑疾病重塑功能信息组织而非仅改变连接强度。这些结果确立了信息分解脑图作为基于rs-fMRI诊断的可解释性表征。代码可在该URL获取。
英文摘要
Resting-state functional magnetic resonance imaging (rs-fMRI) has enabled non-invasive mapping of functional brain interactions for computer-aided diagnosis, yet most existing approaches reduce inter-regional relationships to correlation-based edge weights. Such representations capture co-fluctuation strength but obscure how information is shared across brain regions. Because brain disorders may disrupt not only connectivity strength but also the organization of redundancy, uniqueness and synergy, traditional functional connectivity may miss disease-relevant information structures. Here we introduce IID-GCN, an interpretable graph learning framework that decomposes rs-fMRI interactions into redundancy, uniqueness and synergy graphs using partial entropy decomposition. These information-specific graphs separately characterize shared, region-specific and jointly emergent components of brain activity. A multi-channel graph convolutional network then integrates the decomposed graphs through edge recalibration, cross-information interaction, ROI-attention readout and channel-attentive fusion. Across three datasets, IID-GCN consistently captures complementary diagnostic information beyond traditional functional connectivity. The learned information profiles reveal disorder-specific patterns of altered redundancy, uniqueness and synergy, suggesting that brain diseases reshape functional information organization rather than merely changing connection strength. These results establish information-decomposed brain graphs as an interpretable representation for rs-fMRI-based diagnosis. Our code is available at https://github.com/Zdy12/IID-GCN.
发表机构
- School of Mathematics and Statistics, Shandong University（山东大学数学与统计学院）
- Data Science Institute, Shandong University（山东大学数据科学研究院）
- Academy of Mathematics and Systems Science, University of Chinese Academy of Sciences（中国科学院大学数学与系统科学研究院）
机构由 AI 辅助整理，请以论文原文为准。
