AI and Network Theory Approaches for Studying and Diagnosing Brain Disorders

Neurological and mental disorders represent a significant and growing global health challenge, characterized by late detection, diagnostic subjectivity, and a lack of objective biomarkers. This article explores the convergence of network theory and artificial intelligence (AI) as a powerful framework to address these issues. Network theory provides quantitative, interpretable metrics to describe the brain's structural and functional organization, framing many disorders as “dysconnectivity syndromes.” AI and machine learning techniques, particularly graph neural networks (GNNs) and contrastive methods, are capable of identifying complex, multidimensional patterns in these network metrics, revealing disease subtypes and building predictive diagnostic models. We present case studies on Major Depressive Disorder and Autism Spectrum Disorder, demonstrating the superior performance of GNNs in classification tasks and the ability of contrastive learning to isolate disorder-specific functional connectivity patterns. The integration of network neuroscience and AI emerges as the most promising path toward developing reliable, objective biomarkers for early diagnosis and personalized treatment of brain disorders.