mlsanityData to 14 Oct 2026

Beyond Site Agreement: Re-estimation for Brain Network Generalization

arXiv 2609.34611 ↗First posted 28 Sep 2026cs.LGcited by 0

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Abstract (arXiv)

Cross-site out-of-distribution (OOD) generalization in resting-state functional magnetic resonance imaging (rs-fMRI) often relies on learning task-discriminative representations from full-scan functional connectivity (FC) graphs and promoting invariance across source sites. However, FC graphs are estimated from finite, temporally correlated blood-oxygen-level-dependent (BOLD) sequences. Cross-site agreement therefore does not necessarily imply that predictive evidence remains supported under FC re-estimation within the same scan. In this paper, we propose Brain Network Re-estimation-Informed OOD Learning (BRIO), a framework that uses within-scan FC re-estimation to guide cross-site alignment. BRIO maps fullscan graphs and their re-estimates into consistently indexed connectome factors, enabling comparisons of their predictive contributions. It assesses re-estimation support from changes in these contributions relative to within-class subject variability and class separation. For each source-site pair and class, this task-calibrated support from both sites is combined with predictive relevance to form pairwise qualifications, which determine relative factor weights and overall alignment strength. Leave-one-site-out experiments on four real-world datasets (ABIDE, REST-metaMDD, SRPBS, and ABCD) show that BRIO consistently outperforms competitive baselines, with relative improvements of up to 3.8% in accuracy. These gains also persist under an alternative brain parcellation on ABIDE.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 60 references that are in the map. First posted on arXiv 28 Sep 2026.

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27 of 60 references.

References · 27

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15 more
  1. 2022Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysiscited by 59
  2. 2024SGAC: A Graph Neural Network Framework for Imbalanced and Structure-Aware AMP Classificationcited by 11
  3. 2025ProtoMol: Enhancing Molecular Property Prediction via Prototype-Guided Multimodal Learningcited by 11
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  15. 2025VarCoNet: A variability-aware self-supervised framework for functional connectome extraction from resting-state fMRIcited by 1

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