发表机构
Tarbiat Modares University（塔比阿特莫达勒斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文综述了连接组图学习中的不确定性量化方法，并通过案例证明未校准的GNN预测存在严重过度自信，强调UQ与校准对临床可靠部署不可或缺。
AI 中文摘要
尽管图神经网络（GNNs）在基于连接组的诊断分类中显示出巨大潜力，但确定性模型不可避免地抑制了流程引入的噪声和模型歧义，导致过度自信的预测。尽管不确定性量化（UQ）在体素级分割中被广泛采用，但其在连接组图学习中的作用在很大程度上仍未得到解决。本文针对连接组图学习定制了UQ框架的全面叙述性综述，并附有一项实证案例研究，展示了未校准预测的危害。我们描述了跨神经影像流程的偶然不确定性和认知不确定性的来源，并回顾了主要的UQ范式，从贝叶斯近似和集成方法到证据学习和保形预测。在我们的案例研究中，一个在SUDMEX CONN数据集上基于动态功能连接（dFC）矩阵训练的时序图注意力网络（GAT）对可卡因使用障碍实现了80.0%的诊断准确率（F1=0.794）。然而，通过蒙特卡洛dropout进行的事后不确定性审计揭示了严重的过度自信（ECE=0.127），被错误分类的受试者被赋予高达95%的预测置信度。这种判别能力与校准之间的经验性分歧凸显了深度连接组学中的置信度悖论。我们的研究结果表明，严格的不确定性量化、校准和选择性预测机制对于在临床神经科学中部署可信的基于图的生物标志物是不可或缺的。
英文摘要
While graph neural networks (GNNs) have shown substantial promise in connectome-based diagnostic classification, deterministic models inevitably suppress pipeline-induced noise and model ambiguities, yielding overconfident predictions. Although uncertainty quantification (UQ) is widely adopted in voxel-level segmentation, its role in connectomic graph learning remains largely unaddressed. This paper presents a comprehensive narrative review of UQ frameworks tailored to connectome graph learning alongside an empirical case study demonstrating the perils of uncalibrated predictions. We delineate sources of aleatoric and epistemic uncertainty across neuroimaging pipelines and review prominent UQ paradigms, from Bayesian approximations and ensemble methods to evidential learning and conformal prediction. In our case study, a temporal Graph Attention Network (GAT) trained on dynamic functional connectivity (dFC) matrices from the SUDMEX CONN dataset achieves 80.0% diagnostic accuracy (F1 = 0.794) for Cocaine Use Disorder. However, a post-hoc uncertainty audit via Monte Carlo dropout reveals severe overconfidence (ECE = 0.127), with misclassified subjects assigned prediction confidences up to 95%. This empirical divergence between discrimination and calibration underscores the confidence paradox in deep connectomics. Our findings establish that rigorous UQ, calibration, and selective prediction mechanisms are indispensable for deploying trustworthy graph-based biomarkers in clinical neuroscience.
