Utilizing transfer learning of pre-trained AlexNet and relevance vector machine for regression for predicting healthy older adult’s brain age from structural MRI

Abstract

Discrepancies between the estimated brain age from brain structural MRI and the chronological age have been associated with a broad spectrum of neurocognitive disorders. The performance of brain age estimation heavily depends on predefined or hand-crafted features. Although 3D convolutional neural network (CNN) based approaches have been proposed, they require high computational cost, large memory load, and numerous images. Coupling a pre-trained 2D CNN for transfer learning with a well-established relevance vector machine for regression approach can greatly enhance the capabilities of the model. Several important strategies, including feature transfer learning, 3D feature concatenation, and dimensionality reduction were taken. The estimated brain age was modeled by structural magnetic resonance imaging (sMRI) from 594 normal healthy older individuals (age 50–90 years). We proposed and manifested a pre-trained AlexNet as a robust feature extractor. Also, the considerable cost of developing a 3D CNN was avoided by applying 3D feature concatenation and data reduction. The proposed method achieves superior performance with a mean absolute error of 4.51 years for old subjects. The predicted brain age also demonstrated high test-retest reliability (intra-class correlation coefficient of 0.979). The effectiveness and robustness of the proposed model were well studied. The proposed approach can compete with or even outperform those state-of-the-art approaches, and feature transfer learning strategy can introduce new perspectives to some well-established brain age prediction models with predefined or hand-crafted features.