一种用于零膨胀加权脑连接组分析的贝叶斯自适应潜在混合模型
A Bayesian Adaptive Latent Mixture Model for Zero-Inflated Weighted Brain Connectome Analysis
AI总结：
本文提出一种贝叶斯自适应潜在混合模型，用于处理零膨胀加权网络中的结构零和异质非零边强度，通过共享低秩潜在分数矩阵和hurdle似然分离边存在与条件边强度，实现稳定潜在线性模式和异质主体混合。
摘要：
重复加权网络常常表现出许多结构零和异质非零边强度。在结构连接组学中，这种零膨胀与受体表达重叠而非离散连接模式相吻合。为解决这些特征，我们提出了一种用于零膨胀加权网络的贝叶斯自适应潜在混合模型。我们的方法将每个受体网络表示为共享低秩潜在分数矩阵的简单混合，整合了将边存在与条件边强度分离的hurdle似然。一个稀疏耦合参数使缺失边可以是独立的或有关于潜在连接的。在计算上，我们采用变换后的Hamiltonian Monte Carlo在无约束坐标上进行计算，通过预测拟合、留出链接预测和模板稳定性选择模板数量。理论上，我们建立了后验一致性、局部渐近正态性、Bernstein--von Mises近似和可识别商空间估计量的预测一致性，假设固定模板场景下。模拟显示在混合成员或结构指导稀疏性设置中，模型在拓扑仅基线上的性能提升。应用于人类连接组计划数据时，模型恢复了稳定的潜在分数模式和异质主体混合，行为分析仅作为探索性注释，而非确认生物标志物声明。
英文摘要：
Replicated weighted networks often exhibit many structural zeros alongside heterogeneous non-zero edge strengths. In structural connectomics, this zero-inflation coincides with subjects expressing overlapping, rather than discrete, connectivity patterns. To address these features, we propose a Bayesian adaptive latent mixture model for zero-inflated weighted networks. Our approach represents each subject network as a simplex mixture of shared low-rank latent score matrices, integrated with a hurdle likelihood that separates edge existence from conditional edge strength. A sparsity-coupling parameter enables absent edges to be either independent of, or informative about, the latent connectivity. For computation, we employ transformed Hamiltonian Monte Carlo on unconstrained coordinates, selecting the number of templates via predictive fit, held-out link prediction, and template stability. Theoretically, we establish posterior consistency, local asymptotic normality, a Bernstein--von Mises approximation, and predictive consistency for an identifiable quotient-space estimand under a fixed-template scenario. Simulations demonstrate performance gains over topology-only baselines in settings with mixed memberships or structure-informed sparsity. Applied to Human Connectome Project data, the model recovers stable latent score patterns and heterogeneous subject-level mixtures, with behavioural analyses serving strictly as exploratory annotations rather than confirmatory biomarker claims.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
