Beyond Accuracy: Counterfactual Auditing of fMRI Representations

Abstract

Self-supervised learning is increasingly applied to functional neuroimaging, yet existing evaluations rely mainly on downstream prediction accuracy, offering limited insight into whether learned representations preserve biologically meaningful structure. We propose Generative Manifold Auditing (GMA), a framework that probes the intrinsic structural sensitivity of latent representations via ROI-level counterfactual interventions. Across the multi-site ABIDE benchmark, a denoising autoencoder (DAE) consistently exhibits higher sensitivity to biologically grounded perturbations than contrastive (MoCo) and masked autoencoder (MAE) baselines, which show flattened structural sensitivity despite ap..