Robust Brain Age Estimation based on sMRI via Nonlinear Age-Adaptive Ensemble Learning

Zhang, Zhaonian and Jiang, Richard and Zhang, Ce and Williams, Bryan and Jiang, Ziping and Li, Chang-Tsun and Chazot, Paul L and Pavese, Nicola and Bouridane, Ahmed and Baghdadi, A (2022) Robust Brain Age Estimation based on sMRI via Nonlinear Age-Adaptive Ensemble Learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30. 2146 - 2156. ISSN 1558-0210

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Abstract

Precise prediction on brain age is urgently needed by many bi-omedical areas including mental rehabilitation prognosis as well as various medicine or treatment trials. People began to realize that contrasting physical (real) age and predicted brain age can help to highlight brain issues and evaluate if patients’ brains are healthy or not. Such age prediction is often challenging for sin-gle model-based prediction, while the conditions of brains vary drastically over age. In this work, we present an age-adaptive ensemble model that is based on the combination of four differ-ent machine learning algorithms, including a support vector machine (SVR), a convolutional neural network (CNN) model, and the popular GoogLeNet and ResNet deep networks. The ensemble model proposed here is nonlinearly adaptive, where age is taken as a key factor in the nonlinear combination of various single-algorithm-based independent models. In our age-adaptive ensemble method, the weights of each model are learned automatically as nonlinear functions over age instead of fixed values, while brain age estimation is based on such an age-adaptive integration of various single models. The quality of the model is quantified by the mean absolute errors (MAE) and spearman correlation between the predicted age and the actual age, with the least MAE and the highest Spearman correlation representing the highest accuracy in age prediction. By testing on the Predictive Analysis Challenge 2019 (PAC 2019) dataset, our novel ensemble model has achieved a MAE down to 3.19, which is a significantly increased accuracy in this brain age competition. If deployed in the real world, our novel ensemble model having an improved accuracy could potentially help doc-tors to identify the risk of brain diseases more accurately and quickly, thus helping pharmaceutical companies develop drugs or treatments precisely, and potential offer a new powerful tool for researchers in the field of brain science.