Toward MR protocol-agnostic, unbiased brain age predicted from clinical-grade MRIs.

Autor: Valdes-Hernandez PA; Department of Community Dentistry and Behavioral Science, University of Florida, 1329 SW 16th Street, Ste. 5180, Gainesville, FL, 32610, USA. pvaldeshernandez@dental.ufl.edu.; Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, FL, USA. pvaldeshernandez@dental.ufl.edu.; Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA. pvaldeshernandez@dental.ufl.edu., Laffitte Nodarse C; Department of Community Dentistry and Behavioral Science, University of Florida, 1329 SW 16th Street, Ste. 5180, Gainesville, FL, 32610, USA.; Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, FL, USA.; Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA., Peraza JA; Department of Physics, Florida International University, Miami, FL, USA., Cole JH; Department of Computer Science, Centre for Medical Image Computing, University College London, London, UK.; Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK., Cruz-Almeida Y; Department of Community Dentistry and Behavioral Science, University of Florida, 1329 SW 16th Street, Ste. 5180, Gainesville, FL, 32610, USA.; Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, FL, USA.; Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA.; Department of Neuroscience, College of Medicine, University of Florida, Gainesville, USA.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 Nov 10; Vol. 13 (1), pp. 19570. Date of Electronic Publication: 2023 Nov 10.
DOI: 10.1038/s41598-023-47021-y
Abstrakt: 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.
(© 2023. The Author(s).)
Databáze: MEDLINE
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