Robust Multi-Site ADHD Classification via GraphSAGE-Based Functional Connectivity Modeling from rs-fMRI.

Overview

1. LabRi Laboratory, Ecole Superieure en Informatique, Sidi Bel Abbes 22000, Algeria
2. LIRE Laboratory, Abdelhamid Mehri Constantine 2 University, Constantine 25000, Algeria
3. LISI Laboratory of Intelligent Systems and Informatics, Mila 43000, Algeria
4. LyRIDS, ECE Paris, 10 rue Sextius Michel, 75015 Paris, France
5. Faculty of Electrical Engineering, Electronics and Automation, University of Ruse “Angel Kanchev”, 7017 Ruse, Bulgaria

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is a heterogeneous neurodevelopmental disorder whose diagnosis is mainly based on behavioral assessment and is often delayed due to clinical complexity and limited availability of specialists. Resting-state functional magnetic resonance imaging (rs-fMRI) provides a valuable source of information for supporting automated and objective diagnosis. However, existing studies often do not fully capture the complex interactions of functional connectivity between different brain regions. To address this limitation, this work proposes a graph-based deep learning framework for ADHD classification from rs-fMRI that combines functional connectivity modeling with graph representation learning. The approach used Phase-Locking Value (PLV)-based connectivity estimation and Graph Sample and Aggregate (GraphSAGE) to jointly capture regional brain activity and inter-regional interactions in a scalable and efficient manner. GraphSAGE improves robustness to noise and inter-subject variability by aggregating information from stable local graph neighborhoods. This integration allows the model to learn discriminative connectivity-aware representations while remaining robust to signal variability and adaptable to multi-site data. The proposed framework was evaluated on the publicly available ADHD-200 dataset across multiple acquisition sites as well as on a combined multi-site dataset. The results indicate consistent performance across individual sites and on the combined dataset. The model achieved an Accuracy of 0.89, an AUC of 0.96, and a Specificity of 0.96 on the combined dataset, outperforming several existing methods in this setting. By integrating PLV-based connectivity with GraphSAGE learning, the approach provides an effective and scalable solution for automated ADHD classification from rs-fMRI data, contributing to data-driven approaches for the analysis of neurodevelopmental disorders.

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Data

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Data Availability Statement

The data used in this study are publicly available from the ADHD-200 Preprocessed initiative (Athena pipeline): http://preprocessed-connectomes-project.org/adhd200/ (accessed on 15 February 2025), The code is publicly available at: https://github.com/Rabab070707/ADHD_PLV_GraphSAGE/ (accessed on 13 May 2026).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 2, 28 September 2026

• Funding: added European Commission: BGRRP-2.013-0001

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 7 keywords, 85 references.

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This paper

Bousmaha, R., Meribai, K., Bouchemal, N., Bouchemal, N., & Ivanova, G. (2026). Robust Multi-Site ADHD Classification via GraphSAGE-Based Functional Connectivity Modeling from rs-fMRI. Bioengineering (Basel, Switzerland), 13(5), 586. https://doi.org/10.3390/bioengineering13050586

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