超越站点一致性：脑网络泛化的重估计
Beyond Site Agreement: Re-estimation for Brain Network Generalization
- Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
- Zhengzhou University（郑州大学）
- University Hospital Tübingen（蒂宾根大学医院）
- The Hong Kong Polytechnic University（香港理工大学）
- City University of Hong Kong（香港城市大学）
- The Education University of Hong Kong（香港教育大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出BRIO框架，利用扫描内FC重估计指导跨站点对齐，在四个数据集上准确率提升高达3.8%。
AI中文摘要：
静息态功能磁共振成像（rs-fMRI）中的跨站点分布外（OOD）泛化通常依赖于从全扫描功能连接（FC）图中学习任务判别性表征，并促进跨源站点的不变性。然而，FC图是从有限且时间相关的血氧水平依赖（BOLD）序列中估计的。因此，跨站点一致性并不一定意味着在同一扫描内的FC重估计下预测性证据仍然得到支持。在本文中，我们提出了脑网络重估计信息感知的OOD学习（BRIO）框架，该框架利用扫描内FC重估计来指导跨站点对齐。BRIO将全扫描图及其重估计映射到一致索引的连接组因子中，从而能够比较它们的预测贡献。它通过相对于类内受试者变异性和类分离的这些贡献的变化来评估重估计支持。对于每个源站点对和类别，来自两个站点的任务校准支持与预测相关性相结合，形成成对资格，这些资格决定相对因子权重和整体对齐强度。在四个真实世界数据集（ABIDE、REST-metaMDD、SRPBS和ABCD）上的留一站点外实验表明，BRIO始终优于竞争基线，准确率相对提升高达3.8%。这些增益在ABIDE上的替代脑分区下也持续存在。
英文摘要：
Cross-site out-of-distribution (OOD) generalization in resting-state functional magnetic resonance imaging (rs-fMRI) often relies on learning task-discriminative representations from full-scan functional connectivity (FC) graphs and promoting invariance across source sites. However, FC graphs are estimated from finite, temporally correlated blood-oxygen-level-dependent (BOLD) sequences. Cross-site agreement therefore does not necessarily imply that predictive evidence remains supported under FC re-estimation within the same scan. In this paper, we propose Brain Network Re-estimation-Informed OOD Learning (BRIO), a framework that uses within-scan FC re-estimation to guide cross-site alignment. BRIO maps fullscan graphs and their re-estimates into consistently indexed connectome factors, enabling comparisons of their predictive contributions. It assesses re-estimation support from changes in these contributions relative to within-class subject variability and class separation. For each source-site pair and class, this task-calibrated support from both sites is combined with predictive relevance to form pairwise qualifications, which determine relative factor weights and overall alignment strength. Leave-one-site-out experiments on four real-world datasets (ABIDE, REST-metaMDD, SRPBS, and ABCD) show that BRIO consistently outperforms competitive baselines, with relative improvements of up to 3.8% in accuracy. These gains also persist under an alternative brain parcellation on ABIDE.
