Background The difference between neuroimaging-predicted brain age and chronological age, the predicted age difference (PAD), has been studied as a potential biomarker reflecting individual brain health. Although previous large-scale studies have shown that brain age deviations occur across multiple disorders, cross-disorder comparisons of PAD within a unified framework, together with identification of the neuroimaging features associated with these differences and their related gene expression profiles, remain limited. Our aims are to systematically compare brain aging across multiple common brain disorders and explore the brain patterns and biological processes underlying these differences. Methods and findings In this study, structural MRI data from 45,900 healthy controls (HCs) and 2,698 patients with developmental disorders (attention-deficit/hyperactivity disorder ADHD and autism spectrum disorder ASD), addiction (alcohol use disorder AUD, tobacco use disorder TUD, and AUD p < 0.001 and MCI: d = 0.45, 95% CI 0.34,0.56; p < 0.001), followed by addiction (A p < 0.001, TUD: d = 0.72, 95% CI 0.49,0.96; p < 0.001, and AUD d = 0.62, 95% CI 0.39,0.84; p < 0.001) and p sychiatric disorders (SZ: d = 0.53, 95% CI 0.30,0.76; p < 0.001, BP: d = 0.46, 95% CI 0.22,0.69; p < 0.001 and MDD: d = 0.28, 95% CI 0.11,0.46; p < 0.001), but not different from ex p ected in develo p mental disorders (ASD: d = 0.06, 95% CI −0.04,0.16; p = 0.36) and ADHD: d = 0.01, 95% CI −0.14,0.15; p = 0.98). Furthermore, higher PAD values in patient grou p s were linked to s p ecific spatial brain patterns, including the frontotemporal network in psychiatric disorders, default mode network-salience network-putamen-thalamus in addiction and fronto-occipital network in dementia. Prefrontal cortex involvement was common across disorders, and disorder-specific brain patterns associated genes were enriched in different biological processes. A limitation of our study is that psychiatric disorders and addiction have high comorbidity, and these potential confounders were not considered. Conclusions In summary, the different brain aging patterns, each based around specific underlying circuits, may serve as neuroimaging biomarkers for understanding the neural aging mechanisms in commonly occurring brain disorders. Future studies should test whether these disorder-specific brain aging patterns can serve as useful biomarkers to guide critical clinical decision-making.
Liang et al. (Tue,) studied this question.