Graph Neural Network Analysis of Addiction-Related Brain Networks

Yichen Wu

doi:10.54254/2755-2721/2025.GL27139

Applied and Computational EngineeringOpen access

Graph Neural Network Analysis of Addiction-Related Brain Networks

Research Article

Open Access

Graph Neural Network Analysis of Addiction-Related Brain Networks

Yichen Wu ^1*

¹ Nanjing University of Posts and Telecommunications

^*Corresponding author: p22000814@njupt.edu.cn

Published on 24 September 2025

ACE Vol.186

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-383-3

ISBN (Online): 978-1-80590-384-0

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Abstract

Addiction is considered as a disorder of large-scale brain networks, yet extracting robust biomarkers from high-dimensional connectivity data still remains challenges. With emphasis on attention-based and feature-selection architectures, this paper summarizes recent work applying graph neural networks to addiction-related brain connectivity. This paper introduces the theoretical foundations of brain graphs based on functional magnetic resonance imaging (fMRI), and detail the feature-selected graph spatial attention network (FGSAN) family and related extensions, and compare their merits in the aspects of classification and interpretability. Attention mechanisms empower adaptive weighting of interactions between regions, while a Bayesian feature selector enforces sparsity to highlight potential biomarkers. Across animal and human studies, these methods often improve classification accuracy and identify biologically plausible nodes. However, they face the limits of small cohorts, scanner variability, and model complexity. This paper concludes that graph neural networks, especially when paired with feature selection and temporal modeling, provide a promising framework for discovering addiction-related signatures. This paper also recommends validating these approaches on larger human datasets, and developing explainable, computationally efficient variants in order to improve translational utility.

Keywords:

Graph neural networks, Addiction, Brain connectivity, Feature selection

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References

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[7]. Google. (2025, August 23). Brain connectivity and addiction fMRI. Google. [Online image]. hotp-6-2022-full.png

[8]. Gong, C., Jing, C., Pan, J., & Wang, S. (2022). Feature-selected graph spatial attention network for addictive brain-networks identification. arXiv preprint arXiv: 2207.00583.

[9]. Gong, C., Chen, X., Mughal, B., & Wang, S., et al. (2023). Addictive brain-network identification by spatial attention recurrent network with feature selection. Brain Informatics, 10(1), Article 2.

[10]. Jing, C., Gong, C., Chen, Z., & Wang, S. (2023). Graph diffusion reconstruction network for addictive brain-networks identification. Brain Informatics. 3974, 133–145.

References

[1]. Parvaz, M. A., Moeller, S. J., & Goldstein, R. Z. (2024). Neuroimaging biomarkers in addiction. medRxiv.

[3]. Mohammadi, H., & Karwowski, W. (2025). Graph neural networks in brain connectivity studies: Methods, challenges, and future directions. Brain Sciences, 15(1), 17.

[6]. Zheng, Y., Zhai, T., Lin, X., et al. (2024). The resting-state brain activity signatures for addictive disorders. Med, 5(3).

[7]. Google. (2025, August 23). Brain connectivity and addiction fMRI. Google. [Online image]. hotp-6-2022-full.png

[8]. Gong, C., Jing, C., Pan, J., & Wang, S. (2022). Feature-selected graph spatial attention network for addictive brain-networks identification. arXiv preprint arXiv: 2207.00583.

[10]. Jing, C., Gong, C., Chen, Z., & Wang, S. (2023). Graph diffusion reconstruction network for addictive brain-networks identification. Brain Informatics. 3974, 133–145.

Cite this article

Wu,Y. (2025). Graph Neural Network Analysis of Addiction-Related Brain Networks. Applied and Computational Engineering,186,25-31.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-FMCE 2025 Symposium: Semantic Communication for Media Compression and Transmission

ISBN: 978-1-80590-383-3(Print) / 978-1-80590-384-0(Online)

Editor: Anil Fernando

Conference website: https://2025.conffmce.org/glasgow.html

Conference date: 24 October 2025

Series: Applied and Computational Engineering

Volume number: Vol.186

ISSN: 2755-2721(Print) / 2755-273X(Online)

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