AI 中文总结
该研究提出贝叶斯边空间回归框架，用于多站点自闭症神经影像全连接组推断，在模拟和ABIDE数据中识别出ASD相关连接的异质性变化，支持分布式神经系统的异质性重组。
AI 中文摘要
自闭症谱系障碍（ASD）与分布式脑系统的异质性改变相关，这给全连接组推断带来了挑战。该难题不仅源于连接数量庞大，还源于由解剖学和功能相关的脑区对所表征的效应间的依赖性。我们提出了一种贝叶斯边空间回归框架，将每个参与者的连接组视为网络值响应，并对无序脑区对的调整后ASD效应进行建模。主要方法学贡献是直接在连接上定义的半正定协方差构造。通过对称端点匹配操作，将解剖学和诊断盲的功能相似性从区域提升到边空间，该操作保留了端点身份且对端点顺序不变。加性贝叶斯层级同时估计解剖学、功能和交互贡献，以及多站点调整和连接特定效应。理论结果确立了协方差有效性和结构化成分的连续嵌套。低秩核表示和精确充分统计量简化实现了无需初步边估计的全连接组计算。模拟显示，该框架对效应表面的恢复能力有所提升，尤其在弱信号情况下。在自闭症脑成像数据交换（ABIDE）中，该框架识别出ASD相关连接的广泛减少与局部增加并存，这种模式支持分布式神经系统的异质性重组，而非均匀的超连接或低连接。在拟合参数化下，功能成分具有最大的结构尺度，表明组织不仅限于解剖学邻近性。
英文摘要
Autism spectrum disorder (ASD) is associated with heterogeneous alterations across distributed brain systems, creating challenges for whole-connectome inference. The difficulty arises not only from the large number of connections, but also from dependence among effects indexed by anatomically and functionally related region pairs. We introduce a Bayesian Edge-Space regression framework that treats each participant's connectome as a network-valued response and models the adjusted ASD effect over unordered brain-region pairs. The main methodological contribution is a positive-semidefinite covariance construction defined directly on connections. Anatomical and diagnosis-blind functional similarities are lifted from regions to edge space through a symmetrized endpoint-matching operation that preserves endpoint identity and is invariant to endpoint ordering. An additive Bayesian hierarchy estimates anatomical, functional, and interaction contributions together with multisite adjustments and connection-specific effects. Theoretical results establish covariance validity and continuous nesting of the structured components. Low-rank kernel representations and an exact sufficient-statistic reduction enable whole-connectome computation without preliminary edgewise estimation. Simulations show improved recovery of the effect surface, particularly under weak signals. In the Autism Brain Imaging Data Exchange, the framework identifies widespread reductions together with localized increases in ASD-associated connectivity. This pattern supports heterogeneous reorganization across distributed neural systems rather than uniform hyper- or hypoconnectivity. Under the fitted parameterization, the functional component has the largest structural scale, indicating organization beyond anatomical proximity alone.
Comments49 pages, 4 main-text figures, 2 main-text tables; supplementary material includes theoretical results, simulation studies, computational diagnostics, and additional figures
