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MRI-based brain age prediction model for children under 3 years old using deep residual network

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Abstract

Early identification and intervention of abnormal brain development individual subjects are of great significance, especially during the earliest and most active stage of brain development in children aged under 3. Neuroimage-based brain’s biological age has been associated with health, ability, and remaining life. However, the existing brain age prediction models based on neuroimage are predominantly adult-oriented. Here, we collected 658 T1-weighted MRI scans from 0 to 3 years old healthy controls and developed an accurate brain age prediction model for young children using deep learning techniques with high accuracy in capturing age-related changes. The performance of the deep learning-based model is comparable to that of the SVR-based model, showcasing remarkable precision and yielding a noteworthy correlation of 91% between the predicted brain age and the chronological age. Our results demonstrate the accuracy of convolutional neural network (CNN) brain-predicted age using raw T1-weighted MRI data with minimum preprocessing necessary. We also applied our model to children with low birth weight, premature delivery history, autism, and ADHD, and discovered that the brain age was delayed in children with extremely low birth weight (less than 1000 g) while ADHD may cause accelerated aging of the brain. Our child-specific brain age prediction model can be a valuable quantitative tool to detect abnormal brain development and can be helpful in the early identification and intervention of age-related brain disorders.

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Requests for materials should be addressed to Long Lu or Hongsheng Liu.

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Acknowledgements

The authors would like to thank all participants who participated in the various studies which are used here.

Funding

This research was funded by the National Natural Science Foundation of China [61936013, 71921002], The National Social Science Fund of China [18ZDA325], The Hubei Provincial Natural Science Foundation of China [2019CFA025], The National Key R&D Program of China [2019YFC012003], The Basic and Applied Basic Research Foundation of Guangdong Province [2022A1515110722], and The Guangdong Provincial Medical Science and Technology Research Fund Project (A2023011).

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  1. Lianting Hu, Qirong Wan, and Li Huang have contributed equally to this work.

Authors and Affiliations

  1. Guangzhou Women and Children’s Medical Center, Guangzhou, 510623, Guangdong, China

    Lianting Hu, Jiajie Tang, Hongsheng Liu & Long Lu

  2. Medical Big Data Center, Guangdong Provincial People’s Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China

    Lianting Hu, Shuai Huang, Xuanhui Chen, Lingcong Kong, Huiying Liang & Guangjian Liu

  3. Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China

    Lianting Hu, Shuai Huang, Xuanhui Chen, Huiying Liang & Guangjian Liu

  4. School of Information Management, Wuhan University, Wuhan, 430072, Hubei, China

    Li Huang, Jiajie Tang, Jingyi Deng & Long Lu

  5. Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, 4330060, Hubei, China

    Qirong Wan

  6. School of Physical Sciences, University of California San Diego, La Jolla, San Diego, CA, 92093, USA

    Xiaohe Bai

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  1. Lianting Hu
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Contributions

Author contributions included conception and study design (Li Huang, Long Lu, and Huiying Liang), data collection or acquisition (Shuai Huang and Hongsheng Liu), preprocessing and quality control of MRI data (Lianting Hu and Jingyi Deng), statistical analysis (Lianting Hu and Li Huang), interpretation of results (Lianting Hu, Li Huang, Qirong Wan, Long Lu, and Huiying Liang), drafting the manuscript work (Li Huang, Lianting Hu, Lingcong Kong, and Long Lu), revising the manuscript critically for important intellectual content (Lianting Hu, Qirong Wan, Li Huang, Guangjian Liu, Jiajie Tang, Xuanhui Chen, Xiaohe Bai, Huiying Liang, and Long Lu), and approval of the final version to be published and agreement to be accountable for the integrity and accuracy of all aspects of the work (all authors).

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This retrospective study was carried out using the opt-out method for the case series of our hospital. The study was approved by the Ethics Committee of the Guangzhou Women and Children’s Medical Center (Approval No. 2021–250) and was conducted in accordance with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Informed consent was waived by our Institutional Review Board because of the retrospective nature of our study.

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Hu, L., Wan, Q., Huang, L. et al. MRI-based brain age prediction model for children under 3 years old using deep residual network. Brain Struct Funct 228, 1771–1784 (2023). https://doi.org/10.1007/s00429-023-02686-z

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