Fig. 1: Multimodal diversity framework.

The figure illustrates the dimensions of multimodal diversity, showcasing data diversity (a) and its integration into personalized models through advanced modeling approaches (b). a depicts data diversity with the hierarchical data layers contributing to brain health determinants, spanning micro- to macro-levels between and within data types. The first level focuses on genetic and epigenetic factors, including genetic risks, mutations, DNA methylation, and social epigenomics. The second level represents multimodal brain models (MBM), encompassing brain microarchitecture, neurotransmission, structures, functions, and global networks. The third level integrates whole-body biology, combining multi-omics data such as transcriptomics, proteomics, metabolomics, and microbiomics with systemic health indicators like cardiometabolic health. The fourth level incorporates behavioral, cognitive, and clinical data (BCC). The fifth level includes exposome factors, such as social determinants (e.g., socioeconomic status, social interactions, and social identities) and physical exposures (e.g., green spaces, nanoplastics, air pollution, and heatwaves). b outlines the diversity in modeling approaches required to effectively integrate multimodal data diversity into brain health research. It emphasizes data diversity, the generation of low-dimensional brain representations, the incorporation of extracerebral influences such as whole-body health and exposome data, and the need to account for heterogeneity and individual trajectories. This framework highlights the potential of multimodal diversity to advance precision medicine by addressing complex brain health determinants and personalizing interventions. SES socioeconomic status.