{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T06:13:59Z","timestamp":1788156839811,"version":"build-2803163510"},"reference-count":40,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01MH123550"],"award-info":[{"award-number":["R01MH123550"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01MH132934"],"award-info":[{"award-number":["R01MH132934"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01MH133843"],"award-info":[{"award-number":["R01MH133843"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01MH134896"],"award-info":[{"award-number":["R01MH134896"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Human Brain Mapping"],"published-print":{"date-parts":[[2026,6,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Neuroimaging is vital in quantifying brain atrophy due to typical aging and due to neurodegenerative diseases. To collect large samples necessary to model lifespan brain development, research consortiums aggregate images acquired across multiple study sites. Previous studies have demonstrated that this multi\u2010site study design can lead to site\u2010related bias, necessitating harmonization of these \u201csite effects.\u201d However, current methodologies are unable to generalize to new sites outside the original harmonized sample, limiting translation to new sites or clinical practice. Here, we propose a method called ComBat\u2010Predict (CB\u2010Predict) building upon the ComBat method for site effect adjustment, which extends to data from a new site with smaller sample sizes and unknown site effects. In data from the Alzheimer's Disease Neuroimaging Initiative, our proposed method mitigates bias and yields high accuracy in predicting cortical thickness measures when generalizing the model to new data. Furthermore, we demonstrate that our proposed harmonization method can reduce site\u2010related variance in centile scores estimated using data from the Lifespan Brain Chart Consortium. Altogether, our results demonstrate that CB\u2010Predict effectively harmonizes new sites and thereby enables effective translation of neuroimaging models to additional samples.<\/jats:p>","DOI":"10.1002\/hbm.70546","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T05:51:22Z","timestamp":1779256282000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["<scp>ComBat<\/scp>\n                    \u2010Predict Enhances Generalizability of Neuroimaging Models to New Sites"],"prefix":"10.1002","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7594-4899","authenticated-orcid":false,"given":"Yao","family":"Xin","sequence":"first","affiliation":[{"name":"Department of Public Health Sciences Medical University of South Carolina  Charleston South Carolina 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