Background <p>Advanced brain aging is closely associated with late-onset psychoses, including bipolar disorder(BD), schizophrenia(SP), and major depressive disorder(MDD). Studies have shown that neuroimmune homeostasis imbalance plays an important role in the progression of these conditions. However, potential pathogenetic features of these different late-onset psychoses in the context of brain aging, as well as the crosstalk between aging and these psychiatric conditions, remain to be fully elucidated.</p> Methods <p>To further explore key interrelationships between aging and psychoses systematically, a series of work was designed: First, machine learning was used to identify aging and disease-related features and an aging score was devised. These results were further used to optimize a series of mapping models to explore potential relationships between aging and disease. Subsequently a number of bioinformatics methods, including sensitivity analysis, enrichment analysis, and network analysis were conducted to explore crucial links between aging and these tested diseases.</p> Results <p>Shared features and specific characteristics of these psychoses in the context of brain aging are identified. The neuroimmune homeostasis is highlighted, and changes affecting cellular homeostasis, vascular homeostasis and the heart-brain axis are also integrated. Furthermore, various potential pathogenic features characterize the individual conditions. These results support the importance of neuro-immunosenescence and vascular-lymphatic aging theories.</p> Conclusions <p>By systematically exploring the aging index in different late-onset psychoses, this work integrates various risk factors and presents an approach to apply to other aging-related diseases. It provides a computational framework for future therapeutic interventions targeting the interplay between aging and psychosis.</p>