Feasibility of brain age predictions from clinical T1-weighted MRIs

• Published in: Brain research bulletin Vol. 205; p. 110811
• Main Authors: Valdes-Hernandez, Pedro A, Laffitte Nodarse, Chavier, Cole, James H, Cruz-Almeida, Yenisel
• Format: Journal Article
• Language: English
• Published: United States Elsevier 01.12.2023
• Subjects: Brain age bias; Brain-PAD; DeepBrainNet; Patients; Brain age bias; DeepBrainNet; Brain-PAD; Patients
• ISSN: 0361-9230; 1873-2747; 1873-2747
• DOI: 10.1016/j.brainresbull.2023.110811

An individual's brain predicted age minus chronological age (brain-PAD) obtained from MRIs could become a biomarker of disease in research studies. However, brain age reports from clinical MRIs are scant despite the rich clinical information hospitals provide. Since clinical MRI protocols are meant for specific clinical purposes, performance of brain age predictions on clinical data need to be tested. We explored the feasibility of using DeepBrainNet, a deep network previously trained on research-oriented MRIs, to predict the brain ages of 840 patients who visited 15 facilities of a health system in Florida. Anticipating a strong prediction bias in our clinical sample, we characterized it to propose a covariate model in group-level regressions of brain-PAD (recommended to avoid Type I, II errors), and tested its generalizability, a requirement for meaningful brain age predictions in new single clinical cases. The best bias-related covariate model was scanner-independent and linear in age, while the best method to estimate bias-free brain ages was the inverse of a scanner-independent and quadratic in brain age function. We demonstrated the feasibility to detect sex-related differences in brain-PAD using group-level regression accounting for the selected covariate model. These differences were preserved after bias correction. The Mean-Average Error (MAE) of the predictions in independent data was ∼8 years, 2-3 years greater than reports for research-oriented MRIs using DeepBrainNet, whereas an R (assuming no bias) was 0.33 and 0.76 for the uncorrected and corrected brain ages, respectively. DeepBrainNet on clinical populations seems feasible, but more accurate algorithms or transfer-learning retraining is needed.