Deep-learning based multi-modal models for brain age, cognition and amyloid pathology prediction.

Journal: Alzheimer's research & therapy
Published Date: May 31, 2025

Abstract

BACKGROUND: Magnetic resonance imaging (MRI), combined with artificial intelligence techniques, has improved our understanding of brain structural change and enabled the estimation of brain age. Neurodegenerative disorders, such as Alzheimer's disease (AD), have been linked to accelerated brain aging. In this study, we aimed to develop a deep-learning framework that processes and integrates MRI images to more accurately predict brain age, cognitive function, and amyloid pathology.

Authors

  • Chenxi Wang
    Laboratory of Cell Engineering, Institute of Biotechnology, Research Unit of Cell Death Mechanism, Chinese Academy of Medical Science, 2021RU008, Beijing 100071, China.
  • Weiwei Zhang
    Department of Laboratory Medicine, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China.
  • Ming Ni
    Department of Orthopaedics, Chinese People's Liberation Army General Hospital (301 Hospital), 28 Fuxing Rd, 100853, Beijing, China.
  • Qiong Wang
    Beijing Meiling Biotechnology Corporation, Beijing, 102600, PR China.
  • Chang Liu
    Key Lab of Cell Differentiation and Apoptosis of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Linbin Dai
    Department of Neurology, Institute on Aging and Brain Disorders, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, People's Republic of China.
  • Mengguo Zhang
    Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
  • Yong Shen
  • Feng Gao
    Department of Statistics, UCLA, Los Angeles, CA 90095, USA.

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