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Robust Multi-site Adhd Classification Via Graphsage-based Functional Connectivity Modeling From Rs-fmri.

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Robust Multi-site Adhd Classification Via Graphsage-based Functional Connectivity Modeling From Rs-fmri.

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

Rabab Bousmaha¹, Khouloud Meribai¹, Nardjes Bouchemal^2,3

¹LabRi Laboratory, Ecole Superieure en Informatique, Sidi Bel Abbes 22000, Algeria.

Bioengineering (Basel, Switzerland)

|May 27, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a graph-based deep learning framework using resting-state fMRI for Attention Deficit Hyperactivity Disorder (ADHD) classification. The novel approach achieves high accuracy in identifying ADHD, offering a more objective diagnostic tool.

Keywords:

ADHD GraphSAGE PLV functional connectivity graph-based deep learning multi-site data resting-state fMRI

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Area of Science:

Neuroscience
Artificial Intelligence
Medical Imaging

Background:

Attention Deficit Hyperactivity Disorder (ADHD) diagnosis relies on behavioral assessment, often leading to delays.
Resting-state functional magnetic resonance imaging (rs-fMRI) offers potential for objective ADHD diagnosis.
Existing rs-fMRI studies struggle to fully capture complex brain region interactions.

Purpose of the Study:

To develop a graph-based deep learning framework for automated ADHD classification using rs-fMRI.
To combine functional connectivity modeling with graph representation learning for improved diagnostic accuracy.
To create a scalable and robust model adaptable to multi-site data.

Main Methods:

Utilized Phase-Locking Value (PLV) for functional connectivity estimation.
Employed Graph Sample and Aggregate (GraphSAGE) for graph representation learning.
Integrated regional brain activity and inter-regional interactions for classification.

Main Results:

The framework demonstrated consistent performance across individual and combined multi-site datasets.
Achieved high classification metrics: 0.89 Accuracy, 0.96 AUC, and 0.96 Specificity on the combined dataset.
Outperformed several existing methods in ADHD classification from rs-fMRI data.

Conclusions:

The proposed framework offers an effective and scalable solution for automated ADHD classification from rs-fMRI.
Integrating PLV-based connectivity with GraphSAGE learning enhances diagnostic capabilities.
Contributes to advancing data-driven approaches for neurodevelopmental disorder analysis.