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3DCNN predicting brain age using diffusion tensor imaging

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Abstract

Neuroimaging-based brain age prediction using deep learning is gaining popularity. However, few studies have attempted to leverage diffusion tensor imaging (DTI) to predict brain age. In this study, we proposed a 3D convolutional neural network model (3DCNN) and trained it on fractional anisotropy (FA) data from six publicly available datasets (n = 2406, age = 17–60) to estimate brain age. Implementing a two-loop nested cross-validation scheme with a tenfold cross-validation procedure, we achieved a robust prediction performance of a mean absolute error (MAE) of 2.785 and a correlation coefficient of 0.932. We also employed Grad-Cam++ to visualize the salient features of the proposed model. We identified a few highly salient fiber tracts, including the genu of corpus callosum and the left cerebellar peduncle, among others that play a pivotal role in our model. In sum, our model reliably predicted brain age and provided novel insight into age-related changes in brains’ axonal structure.

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Acknowledgements

Data used in this article were obtained from the Center Of Biomedical Research Excellence (COBRE, available at http://schizconnect.org/), the Beijing Normal University, State Key Laboratory of Cognitive Neuroscience and Learning Enhanced Sample (Beijing-Enhanced, available at http://fcon_1000.projects.nitrc.org/indi/retro/BeijingEnhanced.html), the Cambridge Centre for Ageing and Neuroscience (Cam-CAN, available at https://www.cam-can.org/index.php?content=dataset), the Human Connectome Project (HCP, available at http://db.humanconnectome.org/), the Parkinson’s Progression Markers Initiative (PPMI, available at https://ida.loni.usc.edu/login.jsp?project=&page=HOME), and the Southwest University Longitudinal Imaging Multimodal Brain Data Repository (SLIM, available at http://fcon_1000.projects.nitrc.org/indi/retro/southwestuni_qiu_index.html).

This work was supported in part by the High Performance Computing Center of Central South University.

Funding

YT is supported by grant MIMS20-08 from the Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, 410008, Hunan, People’s Republic of China

    Yuqi Wang, Jingxi Wen, Jiang Xin, Yunhao Zhang & Yan Tang

  2. Department of Psychology, University of Maryland, College Park, MD, 20742, USA

    Hua Xie

  3. Center for Neuroscience Research, Children’s National Hospital, Washington, D.C, 20010, USA

    Hua Xie

  4. Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, 541004, Guangxi, People’s Republic of China

    Yan Tang

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  1. Yuqi Wang
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Wang, Y., Wen, J., Xin, J. et al. 3DCNN predicting brain age using diffusion tensor imaging. Med Biol Eng Comput 61, 3335–3344 (2023). https://doi.org/10.1007/s11517-023-02915-x

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