Work

Reliable Evaluation of Transductive Population Graph Neural Networks for Multisite Autism fMRI

Year: 2026

Type: preprint

Abstract: Reliable population-graph evaluation requires separating cohort-conditioned discrimination from performance at an unseen site. We evaluate a recovered site-aware graph neural network in ABIDE-I (871 participants, 20 sites), distinguishing archived intervention results from newly recomputed controls. Archived cohort AUC was approximately 0.94, with high discrimination retained by site-only connectivity. A reconstructed ten-fold empirical site-prior classifier achieved pooled out-of-fold AUC 0.506 (95% site-resampling interval [0.418, 0.546]); simple site proportions therefore did not reproduce the high cohort result. In a budget-matched support-location experiment, the across-site mean AUC difference was +0.252 [0.184, 0.322] with directed edges but −0.003 [−0.020, 0.015] after symmetrisation, demonstrating implementation sensitivity rather than a stable support-location advantage. A supplementary leave-one-site-out analysis compared scoring-time population-edge access after the same source-site training procedure. Mean site-macro AUC was 0.577 with access and 0.580 without it; the paired difference was −0.0037 [−0.0179, 0.0111]. These observations do not establish equivalence or a complete mechanism. Explicit access contracts, compatible estimators and run-level provenance are necessary to interpret high transductive cohort performance.

Source: bioRxiv (Cold Spring Harbor Laboratory)

DOI: 10.64898/2026.09.15.751719