Benchmarking External Generalization of SPD Matrix Learning for Resting-State fMRI Connectome Prediction

2026-08-31

AI Summary

研究通过引入六个rs-fMRI数据集的年龄预测基准，评估了SPD矩阵学习方法在外部数据集上的泛化能力，发现整体性能受年龄范围不匹配和队列异质性影响显著。

Abstract

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.

Problem

• resting-state fMRI
• external generalization
• SPD matrix learning
• connectome prediction

Innovation

• SPD Matrix Learning
• Resting-State fMRI
• External Generalization
• Age Prediction Benchmark
• Riemannian Harmonization