Federated BrainAge estimation from MRI: a proof of concept

Abstract

AbstractBackgroundThe gap between chronological and biological brain‐age, represented by MRI features, associates with dementia risk (Wang et. al., He et. al.). Age‐scores are predicted, however, using deep learning models, which require large amounts of MRI data. This poses a challenge, as patient brain MRI data is highly privacy‐sensitive and legal barriers often impede the sharing or transferring of data. Federated learning provides a potential solution by enabling training a global model on distributed data, allowing biobanks to keep data local while creating a shared model. In this study, we assess whether a federated set‐up is feasible for brain‐age estimation.MethodWe set up a federated learning framework across three biobanks (Fig. 2) to train a BrainAge model (Wang et. al). Data of healthy individuals from the ADNI dataset was randomly split across the three biobanks (N = 221, 219, 217). MRI images were pre‐processed into gray matter density maps (Good et. al.). The model was iteratively trained using federated averaging (McMahan et. al.) on data at two biobanks (70/30 training/validation split), and tested at the remaining biobank. We trained the model for ten rounds, and optimized for the number of epochs, learning rate, and learning decay hyperparameters. These results were compared to a centrally trained model, where the training samples were pooled together. Additionally, we applied the trained model to individuals with Mild Cognitive Impairment and Alzheimer’s Disease and performed a survival analysis. We assessed performance using Mean Absolute Error (MAE).ResultFederated learning (MAE = 4.02) achieves similar test performance as central learning (MAE = 3.94) (Table 1). The model yielded an MAE of 4.89 for MCI and 5.45 for AD patients; this increase may be explained by their neurodegeneration. We also see a statistically different survival curve for developing AD in individuals with a brain‐age‐gap (Fig. 1), displaying an ‘older’ profile for their chronological age. This might indicate higher risk of developing AD, in line with previous results (Wang et. al.).ConclusionResults show federated learning has the potential to address the challenge of data accessibility for machine learning in complex and data hungry research, including dementia.