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This framework was developed using regional morphometric data from 37,407 healthy individuals (53% female; aged 3\u201390 years) following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The Multivariate Factorial Polynomial Regression (MFPR) emerged as the preferred algorithm optimized using nonlinear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins, and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3,000 study participants. The model and scripts described here are freely available through CentileBrain (\n                  <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/centilebrain.org\/\">https:\/\/centilebrain.org\/<\/jats:ext-link>\n                  ).\n                <\/jats:p>","DOI":"10.1101\/2023.01.30.523509","type":"posted-content","created":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T21:53:22Z","timestamp":1675202002000},"source":"Crossref","is-referenced-by-count":10,"title":["Normative Modeling of Brain Morphometry Across the Lifespan Using CentileBrain: Algorithm Benchmarking and Model Optimization"],"prefix":"10.64898","author":[{"given":"Ruiyang","family":"Ge","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuetong","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi 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