Unraveling Parkinson's disease motor subtypes: A deep learning approach based on spatiotemporal dynamics of EEG microstates.

Journal: Neurobiology of disease
PMID: 40274133

Abstract

BACKGROUND: Despite prior studies on early-stage Parkinson's disease (PD) brain connectivity and temporal patterns, differences between tremor-dominant (TD) and postural instability/gait difficulty (PIGD) motor subtypes remain poorly understood. Our study aims to understand the contribution of altered brain network dynamics to heterogeneous motor phenotypes in PD for improving personalized treatment.

Authors

  • Lin Meng
    Division of Biomedical Engineering, School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK. menglynn@hotmail.com.
  • Deyu Wang
    Department of Pathology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Jun Ma
    State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China.
  • Yu Shi
    NIH BD2K Program Centers of Excellence for Big Data Computing-KnowEng Center, Department of Computer Science, University of Illinois at Urbana-Champaign , Champaign, Illinois.
  • Hongbo Zhao
    School of Medical Technology, Xi'an Medical University, Xi'an, Shaanxi, China.
  • Yanlin Wang
    Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China.
  • Qingqing Shi
    Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China.
  • Xiaodong Zhu
    Department of Medical Oncology, Shanghai Key Laboratory of Medical Epigenetics, Fudan University Shanghai Cancer Center, Institutes of Biomedical Sciences, Fudan University, 270 Dong An Rd, Shanghai, 200032, China. xddr001@163.com.
  • Dong Ming
    Institute of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.

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