Serotonin Neurobiology and Artificial Intelligence-Driven Biomarker Detection in Affective Disorders: A Comprehensive Review

Abstract

Affective disorders, including major depressive disorder (MDD) and generalized anxiety disorder (GAD), impose a major global health burden because of their high prevalence, early onset, disability, and substantial social and economic impacts. Diagnosis remains challenging because it relies largely on subjective clinical assessment and symptom-based criteria, which often delay recognition and contribute to heterogeneous treatment response. Serotonin (5-hydroxytryptamine, 5-HT) has long been central to the understanding of these disorders, but the classical serotonin hypothesis has evolved into a broader systems-level framework in which serotonergic dysfunction is linked with neurotransmission, receptor signaling, transporter regulation, neuroplasticity, endocrine balance, immune modulation, circadian rhythm control, and gut-brain communication. Serotonin biosynthesis, metabolism, and compartmentalization are tightly regulated, and variation in the serotonin transporter and related pathways influences vulnerability to disease, symptom expression, and response to therapy. Although serotonin-targeted pharmacotherapy remains foundational, its delayed and variable clinical effects highlight the need for objective biomarkers and more-precise monitoring strategies. This Review gives a comprehensive overview of serotonin neurobiology in affective disorders and the therapeutic implications of serotonergic dysregulation. It discusses the current understanding of serotonin synthesis, receptor heterogeneity, transporter function, and broader system interactions that shape mood, stress responsivity, and treatment outcomes. The Review also examines the growing role of artificial intelligence in psychiatric biomarker discovery, including machine learning and deep learning approaches applied to neuroimaging, electrophysiology, physiological signals, and language-based data for improved diagnosis and prediction of antidepressant responses. In addition, emerging serotonin-specific biosensors are discussed as promising tools for real-time, noninvasive neurochemical monitoring. By integrating mechanistic insights into serotonin biology with AI-based analytics and biosensing technologies, this Review highlights a pathway toward more objective, personalized, and effective management of affective disorders.