Low-Rank Tensor Fusion for Enhanced Deep Learning-Based Multimodal Brain Age Estimation

Background/Objectives: A multimodal brain age estimation model could provide enhanced insights into brain aging. However, effectively integrating multimodal neuroimaging data to enhance the accuracy of brain age estimation remains a challenging task. Methods: In this study, we developed an innovative data fusion technique employing a low-rank tensor fusion algorithm, tailored specifically for deep learning-based frameworks aimed at brain age estimation. Specifically, we utilized structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and magnetoencephalography (MEG) to extract spatial-temporal brain features with different properties. These features were fused using the low-rank tensor algorithm and employed as predictors for estimating brain age. Results: Our prediction model achieved a desirable prediction accuracy on the independent test samples, demonstrating its robust performance. Conclusions: The results of our study suggest that the low-rank tensor fusion algorithm has the potential to effectively integrate multimodal data into deep learning frameworks for estimating brain age.