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Brain imaging coupled with artificial intelligence has been previously proposed for diagnostic support, but most of these methods have been trained to discriminate only isolated diseases from controls. Here, we develop a novel machine learning framework, named <jats:italic>lifespan tree<\/jats:italic> of brain anatomy, dedicated to the differential diagnosis between multiple diseases simultaneously. It integrates the modeling of volume changes for 124 brain structures during the lifespan with nonlinear dimensionality reduction and synthetic sampling techniques to create easily interpretable representations of brain anatomy over the course of disease progression. As clinically relevant proof\u2010of\u2010concept applications, we constructed a <jats:italic>cognitive lifespan tree<\/jats:italic> of brain anatomy for the differential diagnosis of six causes of neurodegenerative dementia and a <jats:italic>motor lifespan tree<\/jats:italic> of brain anatomy for the differential diagnosis of four causes of parkinsonism using 37,594 MRIs as a training dataset. This original approach significantly enhanced the efficiency of differential diagnosis in the external validation cohort of 1754 cases, outperforming existing state\u2010of\u2010the\u2010art machine learning techniques. <jats:italic>Lifespan tree<\/jats:italic> holds promise as a valuable tool for differential diagnosis in relevant clinical conditions, especially for diseases still lacking effective biological markers.<\/jats:p>","DOI":"10.1002\/hbm.70336","type":"journal-article","created":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T12:09:54Z","timestamp":1756728594000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Lifespan Tree of Brain Anatomy: Diagnostic Values for Motor and Cognitive Neurodegenerative 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