Characterizing brain network alterations in cervical spondylotic myelopathy using static and dynamic functional network connectivity and machine learning.

Journal: Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
PMID: 39823911

Abstract

BACKGROUND: Cervical spondylotic myelopathy (CSM) is a debilitating condition that affects the cervical spine, leading to neurological impairments. While the neural mechanisms underlying CSM remain poorly understood, changes in brain network connectivity, particularly within the context of static and dynamic functional network connectivity (sFNC and dFNC), may provide valuable insights into disease pathophysiology. This study investigates brain-wide connectivity alterations in CSM patients using both sFNC and dFNC, combined with machine learning approaches, to explore their potential as biomarkers for disease classification and progression.

Authors

  • Jiyuan Yao
    Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China.
  • Bingyong Xie
    Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China.
  • Haoyu Ni
    Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China.
  • Zhibin Xu
    Center of Marine Development, Macau University of Science and Technology, Macau, 999078, China; The Institute of Sustainable Development, Macau University of Science and Technology, Macau, 999078, China.
  • Haoxiang Wang
  • Sicheng Bian
    Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China.
  • Kun Zhu
    Aviation Technology Research Institute, China Aerospace Science and Industry Corporation, Beijing, 100143, China.
  • Peiwen Song
    Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China.
  • Yuanyuan Wu
    Department of Mathematics, Southeast University, Nanjing 210096, China; College of Electric and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China.
  • Yongqiang Yu
    College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China.
  • Fulong Dong
    Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China. Electronic address: dongfulongtg@sina.com.

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