Structural connectivity centrality changes mark the path toward Alzheimer's disease
作者: Luis R. PerazaAntonio Díaz-ParraOliver KennionDavid MoratalJohn-Paul TaylorMarcus KaiserRoman Bauer
作者单位: 1Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom
2Center for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Valencia, Spain
3Interdisciplinary Computing and Complex Biosystems Research Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
4Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom
刊名: Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 2019, Vol.11 , pp.98-107
来源数据库: Elsevier Journal
DOI: 10.1016/j.dadm.2018.12.004
关键词: Alzheimer's diseaseDiffusion MRIStructural brain connectivityNetwork centralityComputational modelingMachine learning
英文摘要: Abstract(#br)Introduction(#br)The pathophysiological process of Alzheimer's disease is thought to begin years before clinical decline, with evidence suggesting prion-like spreading processes of neurofibrillary tangles and amyloid plaques.(#br)Methods(#br)Using diffusion magnetic resonance imaging data from the Alzheimer's Disease Neuroimaging Initiative database, we first identified relevant features for dementia diagnosis. We then created dynamic models with the Nathan Kline Institute-Rockland Sample database to estimate the earliest detectable stage associated with dementia in the simulated disease progression.(#br)Results(#br)A classifier based on centrality measures provides informative predictions. Strength and closeness centralities are the most discriminative features, which are...
全文获取路径: Elsevier  (合作)

  • centrality 中心
  • toward 
  • connectivity 连通性
  • path 小路
  • disease