An integrated computational framework for diversity-sensitive personalized medicine

Diversity in biological, social, and environmental factors plays a central role in shaping brain health and disease. Distinct brain disorders frequently exhibit overlapping clinical phenotypes, despite arising from heterogeneous biological and contextual mechanisms. This convergence challenges conventional, population-averaged approaches, which often fail to capture interindividual variability and lead to limited reproducibility, weak translational potential, and inadequate tools for individual-level characterization. To address these gaps, we propose an integrative computational framework that unites normative models of brain aging ("brain clocks"), high-order interactions, and whole-brain modeling. Brain clocks estimate individualized brain health scores by comparing observed brain features to normative agebased trajectories. Brain high-order interactions capture functional dependencies beyond pairwise connectivity, offering sensitive biomarkers that reflect system-level diversity in aging and neurodegeneration. Whole-brain modeling uses theory-based simulations of individual brain dynamics, supporting the inference of latent mechanisms and the evaluation of targeted perturbations in silico. Together, these approaches form a synergistic framework: normative models provide personalized baselines, high-order interactions enhance sensitivity to complex alterations, and whole-brain simulations enable causal insight and guide potential interventions. By embedding interindividual variability and contextual diversity into each computational layer, this framework moves the field toward precision neuroscience, where assessment, understanding, and treatment are tailored to the individuals' unique biological and social profiles.