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Abstract
The difference between the estimated brain age and the chronological age (‘brain-PAD’) could become a clinical biomarker. However, most brain age models were developed for research-grade high-resolution T1-weighted MRIs, limiting their applicability to clinical-grade MRIs from various protocols. We adopted a dual-transfer learning strategy to develop a model agnostic to modality, resolution, or slice orientation. We retrained a convolutional neural network (CNN) using 6281 clinical MRIs from 1559 patients, among 7 modalities and 8 scanner models. The CNN was trained to estimate brain age from synthetic research-grade magnetization-prepared rapid gradient-echo MRIs (MPRAGEs) generated by a ‘super-resolution’ method. The model failed with T2-weighted Gradient-Echo MRIs. The mean absolute error (MAE) was 5.86–8.59 years across the other modalities, still higher than for research-grade MRIs, but comparable between actual and synthetic MPRAGEs for some modalities. We modeled the “regression bias” in brain age, for its correction is crucial for providing unbiased summary statistics of brain age or for personalized brain age-based biomarkers. The bias model was generalizable as its correction eliminated any correlation between brain-PAD and chronological age in new samples. Brain-PAD was reliable across modalities. We demonstrate the feasibility of brain age predictions from arbitrary clinical-grade MRIs, thereby contributing to personalized medicine.
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1 University of Florida, Department of Community Dentistry and Behavioral Science, Gainesville, USA (GRID:grid.15276.37) (ISNI:0000 0004 1936 8091); University of Florida, Pain Research and Intervention Center of Excellence, Gainesville, USA (GRID:grid.15276.37) (ISNI:0000 0004 1936 8091); University of Florida, Center for Cognitive Aging and Memory, McKnight Brain Institute, Gainesville, USA (GRID:grid.15276.37) (ISNI:0000 0004 1936 8091)
2 Florida International University, Department of Physics, Miami, USA (GRID:grid.65456.34) (ISNI:0000 0001 2110 1845)
3 University College London, Department of Computer Science, Centre for Medical Image Computing, London, UK (GRID:grid.83440.3b) (ISNI:0000 0001 2190 1201); University College London, Dementia Research Centre, Queen Square Institute of Neurology, London, UK (GRID:grid.83440.3b) (ISNI:0000 0001 2190 1201)
4 University of Florida, Department of Community Dentistry and Behavioral Science, Gainesville, USA (GRID:grid.15276.37) (ISNI:0000 0004 1936 8091); University of Florida, Pain Research and Intervention Center of Excellence, Gainesville, USA (GRID:grid.15276.37) (ISNI:0000 0004 1936 8091); University of Florida, Center for Cognitive Aging and Memory, McKnight Brain Institute, Gainesville, USA (GRID:grid.15276.37) (ISNI:0000 0004 1936 8091); University of Florida, Department of Neuroscience, College of Medicine, Gainesville, USA (GRID:grid.15276.37) (ISNI:0000 0004 1936 8091)