一种用于个体化概率估计的混合空间统计学习框架：在多中心自闭症神经影像中的应用
A Hybrid Spatial Statistical Learning Framework for Individualized Probability Estimation: Application to Multisite Autism Neuroimaging
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- St. Edward’s University（圣爱德华大学）
- Rice University（莱斯大学）
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中文总结 AI 辅助
提出混合空间统计学习框架，融合边级连接、网络组织和参与者特征，在多中心自闭症神经影像中提升个体化概率估计的可靠性与可迁移性。
中文摘要 AI 辅助
自闭症谱系障碍是一种异质性神经发育疾病，其功能性脑组织在不同个体和影像中心之间存在差异。静息态功能连接为研究这种差异提供了机会，但分析受到高维性、连接之间的强依赖性以及显著的站点异质性的困扰。我们提出了一种用于多中心自闭症神经影像中个体化概率估计的混合空间统计学习框架。该框架将边级连接、基于图的网络组织和参与者特征作为共同概率目标的互补表示。细尺度连接保留了判别信息，而更广泛的表示在站点偏移下稳定了概率估计。所有预处理和模型开发均在完整的站点留出验证（site-held-out validation）中进行，以评估对未见采集环境的可迁移性。模拟表明，混合模型保留了最强边模型的判别能力，同时随着站点间异质性增加提高了概率准确性。在来自20个ABIDE站点的860名参与者中，混合模型保持了ROC AUC，同时降低了Brier分数和对数损失。这些结果表明，多尺度整合可以提高来自复杂、空间依赖的生物医学数据的个体化概率的可靠性和可迁移性。
英文摘要
Autism spectrum disorder is a heterogeneous neurodevelopmental condition whose functional brain organization varies across individuals and imaging centers. Resting-state functional connectivity provides an opportunity to study this variation, but analysis is complicated by high dimensionality, strong dependence among connections, and substantial site heterogeneity. We introduce a Hybrid Spatial Statistical Learning Framework for individualized probability estimation in multisite autism neuroimaging. The framework combines edge-level connectivity, graph-based network organization, and participant characteristics as complementary representations of a common probability target. Fine-scale connectivity retains discriminatory information, while broader representations stabilize probability estimates under site shift. All preprocessing and model development are performed within complete site-held-out validation to assess transportability to unseen acquisition environments. Simulations show that the hybrid preserves the discrimination of the strongest edge model while improving probability accuracy as between-site heterogeneity increases. In 860 participants from 20 ABIDE sites, the hybrid retained ROC AUC while reducing Brier score and log loss. These results show that multiscale integration can improve the reliability and transportability of individualized probabilities from complex, spatially dependent biomedical data.
