AbstractBackground: Major Depressive Disorder (MDD) is a complex psychiatric condition influenced by neuroinflammatory processes and synaptic plasticity alterations. Understanding these mechanisms is critical for advancing diagnostic and therapeutic strategies. This study integrates meta-analysis and machine learning to explore the relationship between neuroinflammatory markers, synaptic plasticity indicators, and MDD.
Methods and Materials: A meta-analysis of published studies on neuroinflammatory markers (e.g., IL-6, TNF-α, CRP) and synaptic plasticity indicators (e.g., BDNF) was conducted. Data were synthesized using PRISMA guidelines. Machine learning algorithms, including random forests, support vector machines, and neural networks, were applied to analyze the aggregated data, identify biomarker patterns, and develop predictive models.
Results: The meta-analysis revealed significant positive associations between elevated IL-6, TNF-α, and CRP levels and MDD (effect sizes: 0.58–0.72, p<0.005). BDNF levels showed a negative association (-0.48, p = 0.003). Among machine learning models, neural networks achieved the highest performance, with 91.2% accuracy, 89.8% sensitivity, and 92.5% specificity.
Discussion: The findings confirm the intertwined roles of neuroinflammation and synaptic plasticity in MDD. Machine learning effectively identified complex biomarker patterns, supporting its utility in stratifying MDD patients and enabling precision psychiatry. These results align with prior studies on inflammatory and neurotrophic mechanisms in depression.
Conclusion: This study underscores the potential of combining meta-analysis and machine learning to uncover the neurobiological mechanisms of MDD. The integration of computational tools offers promising avenues for biomarker discovery and personalized therapeutic strategies in psychiatric research.
