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Identification of the Neural Circuit Underlying Episodic Memory Deficit in Amnestic Mild Cognitive Impairment via Machine Learning on Gray Matter Volume.
Wang, Zan; Zhang, Zhengsheng; Xie, Chunming; Shu, Hao; Liu, Duan; Zhang, Zhijun.
Afiliação
  • Wang Z; School of Medicine, Southeast University, Nanjing, China.
  • Zhang Z; Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing, China.
  • Xie C; School of Medicine, Southeast University, Nanjing, China.
  • Shu H; Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing, China.
  • Liu D; School of Medicine, Southeast University, Nanjing, China.
  • Zhang Z; Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing, China.
J Alzheimers Dis ; 84(3): 959-964, 2021.
Article em En | MEDLINE | ID: mdl-34602473
ABSTRACT
Based on whole-brain gray matter volume (GMV), we used relevance vector regression to predict the Rey's Auditory Verbal Learning Test Delayed Recall (AVLT-DR) scores of individual amnestic mild cognitive impairment (aMCI) patient. The whole-brain GMV pattern could significantly predict the AVLT-DR scores (r = 0.54, p < 0.001). The most important GMV features mainly involved default-mode (e.g., posterior cingulate gyrus, angular gyrus, and middle temporal gyrus) and limbic systems (e.g., hippocampus and parahippocampal gyrus). Therefore, our results provide evidence supporting the idea that the episodic memory deficit in aMCI patients is associated with disruption of the default-mode and limbic systems.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Memória Episódica / Disfunção Cognitiva / Substância Cinzenta / Aprendizado de Máquina / Rede de Modo Padrão / Amnésia Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Memória Episódica / Disfunção Cognitiva / Substância Cinzenta / Aprendizado de Máquina / Rede de Modo Padrão / Amnésia Idioma: En Ano de publicação: 2021 Tipo de documento: Article