Neuroimaging-derived brain age as a new promising biomarker in mental disorders

Mental disorders are frequently associated with accelerated brain aging, yet the diagnostic and classificatory utility of brain age remains uncertain. This study aimed to evaluate the diagnostic value of brain age across multiple mental disorders and to identify the underlying neural mechanisms. Articles published through November 2025 were retrieved from PubMed, Web of Science, and Embase, resulting in 68 eligible studies covering DSM-5 diagnostic categories. We compared brain age across disorders and extracted key contributing brain regions. The largest effect was observed in schizophrenia spectrum disorders (Cohen’s d = 3.49, 95% CI 2.62–4.37, p < 0.05), followed by neurocognitive disorders (Cohen’s d = 3.27, 95% CI 2.31–4.24, p < 0.05), mood disorders (Cohen’s d = 1.41, 95% CI 0.69–2.14, p < 0.05), and neurodevelopmental disorders (mean = 0.60). Analysis of covariance indicated a significant effect of diagnostic category on brain age (F = 5.13, p = 0.004), and Bonferroni tests further confirmed intergroup differences ( p < 0.05). The central executive, default mode, and salience networks, emerged as a common system implicated across disorders, although the relative contributions differed by diagnosis. Overall, these findings suggest that brain age may serve as a biomarker for the diagnosis and classification of mental disorders. • Brain age serves as a diagnostic biomarker, showing significant elevation in mental disorders. • Distinct mental disorders exhibit heterogeneous effects on brain aging, supported by statistical evidence. • Accelerated brain aging involves disorder-specific neural network patterns across diagnostic categories. • The performance of brain age models varies depending on modeling strategies.