Interpretable MRI-Based Deep Learning for Alzheimer's Risk and Progression.

Journal: medRxiv : the preprint server for health sciences
Published Date: May 7, 2025

Abstract

Timely intervention for Alzheimer's disease (AD) requires early detection. The development of immunotherapies targeting amyloid-beta and tau underscores the need for accessible, time-efficient biomarkers for early diagnosis. Here, we directly applied our previously developed MRI-based deep learning model for AD to the large Chinese SILCODE cohort (722 participants, 1,105 brain MRI scans). The model - initially trained on North American data - demonstrated robust cross-ethnic generalization, without any retraining or fine-tuning, achieving an AUC of 91.3% in AD classification with a sensitivity of 95.2%. It successfully identified 86.7% of individuals at risk of AD progression more than 5 years in advance. Individuals identified as high-risk exhibited significantly shorter median progression times. By integrating an interpretable deep learning brain risk map approach, we identified AD brain subtypes, including an MCI subtype associated with rapid cognitive decline. The model's risk scores showed significant correlations with cognitive measures and plasma biomarkers, such as tau proteins and neurofilament light chain (NfL). These findings underscore the exceptional generalizability and clinical utility of MRI-based deep learning models, especially in large and diverse populations, offering valuable tools for early therapeutic intervention. The model has been made open-source and deployed to a free online website for AD risk prediction, to assist in early screening and intervention.

Authors

  • Bin Lu
    Department of Endocrinology and Metabolism, Huadong Hospital, Fudan University, Shanghai, China.
  • Yan-Rong Chen
    Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.
  • Rui-Xian Li
    Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China.
  • Ming-Kai Zhang
    Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China.
  • Shao-Zhen Yan
    Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing 100053, China.
  • Guan-Qun Chen
    Department of Neurology, Beijing Chaoyang Hospital, Capital Medical University, No.8 Gongti South Road, Chaoyang District, Beijing 100020, China.
  • Francisco Xavier Castellanos
    Department of Child and Adolescent Psychiatry, NYU Robert I. Grossman School of Medicine, New York, NY, USA.
  • Paul M Thompson
    Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Jie Lu
    Department of Endocrinology and Metabolism, the Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China.
  • Ying Han
    Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China.
  • Chao-Gan Yan
    CAS Key Laboratory of Behavioral Science, Institute of Psychology, 16 Lincui Road, Chaoyang District, Beijing, 100101, China. yancg@psych.ac.cn.

Keywords

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