建立跨数据集的脑连接预测基准，检验模型真实泛化能力
Benchmarking External Generalization of SPD Matrix Learning for Resting-State fMRI Connectome Prediction
- 用六大数据集构建可复现的年龄预测基准
- 外推验证下模型性能显著下降，差异缩小
- 适合评估脑影像模型在真实场景中的泛化性
静息态功能磁共振成像（rs-fMRI）功能连接（FC）矩阵广泛用于个体水平预测，但单一队列内表现优异未必能推广到新队列。本文考察当测试数据来自完全独立的rs-fMRI数据集时，模型性能是否仍保持。每例扫描被表示为正则化的对称正定（SPD）相关性连接组，利用SPD流形几何特性。提出一个跨六个数据集（COBRE、ADNIDOD、Cam-CAN、ABIDE、OASIS-3、ADNI）的可复现年龄预测基准，比较向量化相关基线、切空间岭回归、SPDNet及分组瑞曼调和方法。在队列内与合并交叉验证下表现良好，但留一数据集外（LODO）验证下误差上升，各方法差距缩小，性能受年龄范围不匹配和队列异质性影响显著。该基准提供统一输入、模型设置、数据划分与分析脚本，使未来SPD矩阵学习方法可在相同外部验证协议下评估。
原文摘要 · Abstract (English)
Resting-state functional magnetic resonance imaging (rs-fMRI) functional connectivity (FC) matrices are widely used for individual-level prediction, but strong performance within one cohort may not generalize to a new cohort. We ask whether within-dataset performance remains when the test data come from an entirely held-out rs-fMRI dataset. Each scan is represented as a regularized symmetric positive definite (SPD) correlation connectome, which allows methods to use the geometry of the SPD manifold. We introduce a reproducible age-prediction benchmark across six rs-fMRI datasets: COBRE, ADNIDOD, Cam-CAN, ABIDE, OASIS-3, and ADNI. The benchmark compares a vectorized correlation baseline, Tangent-Space Ridge, SPDNet, and split-wise Riemannian harmonization under within-dataset GroupKFold, pooled GroupKFold, and leave-one-dataset-out (LODO) evaluation. Within-dataset and pooled GroupKFold results are substantially more favorable than LODO results. When an entire dataset is held out, prediction error increases, differences among methods narrow, and performance is strongly affected by age-range mismatch and cohort heterogeneity. The benchmark provides common inputs, model settings, data splits, and analysis scripts so that future SPD matrix learning methods can be evaluated under the same external-validation protocol.
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