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Harmonization of tracer\u2010specific biases is crucial for optimal performance of downstream tasks. Here, we investigated the efficacy of ComBat, a data\u2010driven harmonization model, for reducing tracer\u2010specific biases in regional amyloid PET measurements from [\n                    <jats:sup>18<\/jats:sup>\n                    F]\u2010florbetapir (FBP) and [\n                    <jats:sup>11<\/jats:sup>\n                    C]\u2010Pittsburgh compound\u2010B (PiB). One hundred thirteen head\u2010to\u2010head FBP\u2010PiB scan pairs, scanned from the same subject within 90\u2009days, were selected from the Open Access Series of Imaging Studies 3 (OASIS\u20103) dataset. The Centiloid scale, ComBat with no covariates, ComBat with biological covariates, and GAM\u2010ComBat with biological covariates were used to harmonize both global and regional amyloid standardized uptake value ratios (SUVR). Variants of ComBat, including longitudinal ComBat and PEACE, were also tested. Intraclass correlation coefficient (ICC) and mean absolute error (MAE) were computed to measure the absolute agreement between tracers. Additionally, longitudinal amyloid SUVRs from an anti\u2010amyloid drug trial were simulated using linear mixed effects modeling. Differences in rates\u2010of\u2010change between simulated treatment and placebo groups were tested, and change in statistical power\/Type\u2010I error after harmonization was quantified. In the head\u2010to\u2010head tracer comparison, ComBat with no covariates was the best at increasing ICC and decreasing MAE of both global summary and regional amyloid PET SUVRs between scan pairs of the same group of subjects. In the clinical trial simulation, harmonization with both Centiloid and ComBat increased statistical power of detecting true rate\u2010of\u2010change differences between groups and decreased false discovery rate in the absence of a treatment effect. The greatest benefit of harmonization was observed when groups exhibited differing FBP\u2010to\u2010PiB proportions. ComBat outperformed the Centiloid scale in harmonizing both global and regional amyloid estimates. Additionally, ComBat improved the detection of rate\u2010of\u2010change differences between clinical trial groups. Our findings suggest that ComBat is a viable alternative to Centiloid for harmonizing regional amyloid PET analyses.\n                  <\/jats:p>","DOI":"10.1002\/hbm.70068","type":"journal-article","created":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T07:47:54Z","timestamp":1731570474000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Evaluation of\n                    <scp>ComBat<\/scp>\n                    Harmonization for Reducing Across\u2010Tracer Differences in Regional Amyloid\n                    <scp>PET<\/scp>\n                    Analyses"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2558-4132","authenticated-orcid":false,"given":"Braden","family":"Yang","sequence":"first","affiliation":[{"name":"Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis  St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8671-8424","authenticated-orcid":false,"given":"Tom","family":"Earnest","sequence":"additional","affiliation":[{"name":"Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis  St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7213-0734","authenticated-orcid":false,"given":"Sayantan","family":"Kumar","sequence":"additional","affiliation":[{"name":"Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis  St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deydeep","family":"Kothapalli","sequence":"additional","affiliation":[{"name":"Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis  St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8114-0552","authenticated-orcid":false,"given":"Tammie","family":"Benzinger","sequence":"additional","affiliation":[{"name":"Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis  St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2109-2955","authenticated-orcid":false,"given":"Brian","family":"Gordon","sequence":"additional","affiliation":[{"name":"Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis  St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0795-8820","authenticated-orcid":false,"given":"Aristeidis","family":"Sotiras","sequence":"additional","affiliation":[{"name":"Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis  St. Louis Missouri USA"},{"name":"Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St. Louis  St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,11,14]]},"reference":[{"key":"e_1_2_14_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00259-015-3156-9"},{"key":"e_1_2_14_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.117129"},{"key":"e_1_2_14_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/dad2.12436"},{"key":"e_1_2_14_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2015.08.166"},{"key":"e_1_2_14_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.117593"},{"key":"e_1_2_14_7_1","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.121.263195"},{"key":"e_1_2_14_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00259-023-06209-0"},{"key":"e_1_2_14_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/alz.13677"},{"key":"e_1_2_14_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbr.2018.02.017"},{"key":"e_1_2_14_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/alz.12491"},{"key":"e_1_2_14_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.11.024"},{"key":"e_1_2_14_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2013.12.003"},{"key":"e_1_2_14_14_1","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxj037"},{"key":"e_1_2_14_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/ana.20009"},{"key":"e_1_2_14_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2014.07.003"},{"key":"e_1_2_14_17_1","article-title":"OASIS\u20103: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease","author":"LaMontagne P. 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