Graph Neural Network Analysis of Addiction-Related Brain Networks

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.