Benchmarking External Generalization of SPD Matrix Learning for Resting-State fMRI Connectome Prediction
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, T...
| Authors | Ce Ju, Antoine Collas, Florent Bouchard et al. |
|---|---|
| Primary category | eess.SP |
| Published | 31 August 2026 |
| Last updated at source | 31 August 2026 |
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