Machine learning analysis of 120 participants in the landmark FINGER trial revealed that individuals cluster into distinct brain pattern subgroups based on cortical thickness and subcortical volume measurements, with each subgroup responding differently to the comprehensive lifestyle intervention targeting diet, exercise, cognitive training, and vascular health. The clustering approach identified meaningful neuroanatomical subtypes among older adults with elevated cardiovascular risk scores, suggesting that brain structure heterogeneity may explain why some participants benefit more from dementia prevention programs than others. This personalized medicine approach represents a significant advance over one-size-fits-all prevention strategies. The FINGER trial has already established that multidomain interventions can slow cognitive decline, but identifying which brain patterns predict optimal response could revolutionize how we target interventions. The finding aligns with emerging precision medicine paradigms in neurology, where biomarker-guided treatment selection improves outcomes. For the millions of adults pursuing dementia prevention, this suggests future interventions may be tailored based on individual brain imaging profiles rather than applying generic protocols. However, the relatively small sample size of 120 participants limits generalizability, and replication in larger, more diverse cohorts will be essential before clinical implementation of brain pattern-guided prevention strategies.