Brain age prediction using interpretable multi-feature-based convolutional neural network in mild traumatic brain injury.

Journal: NeuroImage
PMID: 39048043

Abstract

BACKGROUND: Convolutional neural network (CNN) can capture the structural features changes of brain aging based on MRI, thus predict brain age in healthy individuals accurately. However, most studies use single feature to predict brain age in healthy individuals, ignoring adding information from multiple sources and the changes in brain aging patterns after mild traumatic brain injury (mTBI) were still unclear.

Authors

  • Xiang Zhang
    Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Yizhen Pan
    The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China.
  • Tingting Wu
  • Wenpu Zhao
    The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China.
  • Haonan Zhang
    Electronic Information School, Wuhan University, Wuhan 430064, China.
  • Jierui Ding
    The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China.
  • Qiuyu Ji
    The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China.
  • Xiaoyan Jia
    The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China.
  • Xuan Li
    College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, China.
  • ZhiQi Lee
    Institute of Cyberspace Security, Qufu Normal University, Jining City, Shandong Province, People's Republic of China.
  • Jie Zhang
    College of Physical Education and Health, Linyi University, Linyi, Shandong, China.
  • Lijun Bai
    Department of Geriatrics, Traditional Chinese Medicine Hospital of Penglai, Yantai 265600, China.

Keywords

External Resources

Popular Topics
Recent Journals