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Accumulated evidence indicates it is important to disease biology. The cortical cytoarchitecture is conventionally accessible only through invasive neuropathological techniques. Diffusion MRI (dMRI) holds the potential to reveal whole\u2010brain cytoarchitecture noninvasively. However, current dMRI signal models are constrained by simplified assumptions, which limit their ability to accurately quantify cortical architecture. Here, we present Diffusion\u2010MRI\u2010based Estimation of Cortical Architecture using Machine\u2010learning (DECAM), a cutting\u2010edge data\u2010driven translational framework capable of accurately and directly mapping the heterogeneous, whole\u2010brain soma density in primates. Leveraging high\u2010resolution multi\u2010shell dMRI and histological datasets of the non\u2010human primate brain, the DECAM deep learning framework is optimized through a novel best response constraint. Cortical label vectors are developed to address dMRI\u2010histology misregistration in primate brains with complex morphology. The DECAM framework is generalizable. It can be further extended for noninvasively estimating other neuropathological measures, such as neurite density, and extended for estimating neuropathological measures in human brains. DECAM generates high\u2010fidelity, reproducible whole\u2010brain soma density maps validated with histology and paves the way for noninvasive virtual histology for translational applications.<\/jats:p>","DOI":"10.1002\/advs.202512752","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T05:33:38Z","timestamp":1767591218000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Diffusion\u2010MRI\u2010Based Estimation of Cortical Architecture via Machine Learning (DECAM) in Primate Brains"],"prefix":"10.1002","volume":"13","author":[{"given":"Tianjia","family":"Zhu","sequence":"first","affiliation":[{"name":"Department of Radiology Children's Hospital of Philadelphia  Philadelphia PA USA"},{"name":"Department of Bioengineering University of Pennsylvania  Philadelphia PA 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