{"doi":"10.21203/rs.3.rs-3229072/v1","title":" Toward MR protocol-agnostic, bias-corrected brain age predicted from clinical-grade MRIs","abstract":"The predicted brain age minus the chronological age ('brain-PAD') could become a clinical biomarker. However, most brain age methods were developed to use research-grade high-resolution T1-weighted MRIs, limiting their applicability to clinical-grade MRIs from multiple protocols. To overcome this, we adopted a double transfer learning approach to develop a brain age model agnostic to modality, resolution, or slice orientation. Using 6,224 clinical MRIs among 7 modalities, scanned from 1,540 patients using 8 scanners among 15 + facilities of the University of Florida's Health System, we retrained a convolutional neural network (CNN) to predict brain age from synthetic research-grade magnetization-prepared rapid gradient-echo MRIs (MPRAGEs) generated by a deep learning-trained 'super-resolution' method. We also modeled the \"regression dilution bias\", a typical overestimation of younger ages and underestimation of older ages, which correction is paramount for personalized brain age-based biomarkers. This bias was independent of modality or scanner and generalizable to new samples, allowing us to add a bias-correction layer to the CNN. The mean absolute error in test samples was 4.67-6.47 years across modalities, with similar accuracy between original MPRAGEs and their synthetic counterparts. Brain-PAD was also reliable across modalities. We demonstrate the feasibility of clinical-grade brain age predictions, contributing to personalized medicine.","journal":"Research Square","year":2023,"id":410256,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.959,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":722104,"name":"Chavier Laffitte Nodarse","orcid":"0000-0002-1018-5016","position":1,"is_corresponding":false},{"id":722106,"name":"Julio A. Peraza","orcid":"0000-0003-3816-5903","position":2,"is_corresponding":false},{"id":54146,"name":"James H. Cole","orcid":"0000-0003-1908-5588","position":3,"is_corresponding":false},{"id":378937,"name":"Yenisel Cruz‐Almeida","orcid":"0000-0002-0065-0236","position":4,"is_corresponding":false},{"id":451868,"name":"Pedro A. Valdés-Hernández","orcid":"0000-0003-2278-0100","position":0,"is_corresponding":true}],"reference_count":27,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:21:35.071397Z","pmid":"37609150","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}