Structural Connectome Harmonization Using Deep Learning: The Strength of Graph Neural Networks

• URL: http://arxiv.org/abs/2507.13992v1
• Date: Fri, 18 Jul 2025 14:58:05 GMT
• Title: Structural Connectome Harmonization Using Deep Learning: The Strength of Graph Neural Networks
• Authors: Jagruti Patel, Thomas A. W. Bolton, Mikkel Schöttner, Anjali Tarun, Sebastien Tourbier, Yasser Alemàn-Gòmez, Jonas Richiardi, Patric Hagmann,
• Abstract summary: Small sample sizes in structural connectome (SC) studies limit the development of reliable biomarkers for neurological and psychiatric disorders.<n>Large-scale multi-site studies have exist, but they have acquisition-related biases due to scanner heterogeneity.<n>We propose a site-conditioned deep harmonization framework that harmonizes SCs across diverse acquisition sites without requiring metadata.
• Score: 0.9663199711697325
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Small sample sizes in neuroimaging in general, and in structural connectome (SC) studies in particular limit the development of reliable biomarkers for neurological and psychiatric disorders - such as Alzheimer's disease and schizophrenia - by reducing statistical power, reliability, and generalizability. Large-scale multi-site studies have exist, but they have acquisition-related biases due to scanner heterogeneity, compromising imaging consistency and downstream analyses. While existing SC harmonization methods - such as linear regression (LR), ComBat, and deep learning techniques - mitigate these biases, they often rely on detailed metadata, traveling subjects (TS), or overlook the graph-topology of SCs. To address these limitations, we propose a site-conditioned deep harmonization framework that harmonizes SCs across diverse acquisition sites without requiring metadata or TS that we test in a simulated scenario based on the Human Connectome Dataset. Within this framework, we benchmark three deep architectures - a fully connected autoencoder (AE), a convolutional AE, and a graph convolutional AE - against a top-performing LR baseline. While non-graph models excel in edge-weight prediction and edge existence detection, the graph AE demonstrates superior preservation of topological structure and subject-level individuality, as reflected by graph metrics and fingerprinting accuracy, respectively. Although the LR baseline achieves the highest numerical performance by explicitly modeling acquisition parameters, it lacks applicability to real-world multi-site use cases as detailed acquisition metadata is often unavailable. Our results highlight the critical role of model architecture in SC harmonization performance and demonstrate that graph-based approaches are particularly well-suited for structure-aware, domain-generalizable SC harmonization in large-scale multi-site SC studies.

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