Machine learning approach effectively discriminates between Parkinson's disease and progressive supranuclear palsy: Multi-level indices of rs-fMRI.

Journal: Brain research bulletin
Published Date: Jul 22, 2025

Abstract

AIM: Parkinson's disease (PD) and progressive supranuclear palsy (PSP) present similar clinical symptoms, but their treatment options and clinical prognosis differ significantly. Therefore, we aimed to discriminate between PD and PSP based on multi-level indices of resting-state functional magnetic resonance imaging (rs-fMRI) via the machine learning approach.

Authors

  • Weiling Cheng
    Department of Critical Care Medicine, Affiliated Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou, Zhejiang, China.
  • Xiao Liang
    Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Veterinary Medicine, China Agricultural University, Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Beijing Laboratory for Food Quality and Safety, Beijing, 100193, People's Republic of China; College of Veterinary Medicine, Qingdao Agricultural University, Qingdao, 266109, People's Republic of China.
  • Wei Zeng
    State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
  • Jiali Guo
    College of Chemistry, Sichuan University, Chengdu610064, People's Republic of China.
  • Zhibiao Yin
    Department of Neurology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
  • Jiankun Dai
    MRI research, GE Healthcare, Beijing, China.
  • Daojun Hong
    Department of Neurology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
  • Fuqing Zhou
    Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang 330006, China; Jiangxi Province Medical Imaging Research Institute, Nanchang, 330006, China. Electronic address: fq.chou@yahoo.com.
  • Fangjun Li
    School of Information Science and Engineering, Shandong University, supported by Shandong Provincial Key Laboratory of Wireless Communication Technologies, Jinan, 250100, China.
  • Xin Fang
    School of Information Science and Technology, University of Science and Technology of China, Hefei 230022, China.

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