Agustín Ibáñez, Claudia Duran‐Aniotz, Joaquín Migeot, Sandra Báez, Sol Fittipaldi, Carlos Coronel‐Oliveros, Harris A. Eyre, Chinedu Udeh‐Momoh, Henrik Zetterberg, Suvarna Alladi, Carmen Sandi, Ian H. Robertson, Sanne Franzen, Temitope Farombi, Janitza L. Montalvo‐Ortiz, Sudha Seshadri, Felipe A. Court, Pedro A. Valdés‐Sosa, Jiayuan Xu, Chunshui Yu, Lea T. Grinberg, Brian Lawlor, Perminder S. Sachdev, Kristine Yaffe, Vladimir Hachinski, Karl Friston, Enzo Tagliazucchi, Hernando Santamaría‐García
The worldwide rise of neurological and psychiatric conditions poses major challenges. However, current global research remains fragmented, dominated by limited cohorts and poorly integrated datasets that disconnect whole-body health, exposome, and brain health. Theories rarely unify brain measures with extracerebral factors or capture heterogeneity in individual trajectories. We introduce multimodal diversity, a non-linear, non-simplistic causal and ecological construct integrating data representation, whole-body and exposomic factors, and computational modeling to address this situated, embedded, and embodied complexity. This heuristic metamodel integrates global, multilevel data into personalized predictions fostering population inclusion, multimodal integration, diagnostic precision, and equitable, context-sensitive advances in brain health. Ibanez et al. introduce multimodal diversity, a synergistic framework integrating multimodal brain metrics, whole-body health, and exposomic data through neurosyndemic computational modeling to advance context-sensitive precision brain health across global settings.