Multi-Order Brain Functional Connectivity Network-Based Machine Learning Method for Recognition of Delayed Neurocognitive Recovery in Older Adults Undergoing Non-cardiac Surgery.

Wu, Guoqing; Jiang, Zhaoshun; Cai, Yuxi; Zhang, Xixue; Lv, Yating; Li, Shihong; Lin, Guangwu; Bao, Zhijun; Liu, Songbin; Gu, Weidong

Wu, Guoqing; Jiang, Zhaoshun; Cai, Yuxi; Zhang, Xixue; Lv, Yating; Li, Shihong; Lin, Guangwu; Bao, Zhijun; Liu, Songbin; Gu, Weidong.

Afiliación

Wu G; School of Information Science and Technology, Fudan University, Shanghai, China.
Jiang Z; Department of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, China.
Cai Y; Department of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, China.
Zhang X; Department of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, China.
Lv Y; Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.
Li S; Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China.
Lin G; Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China.
Bao Z; Department of Geriatric Medicine, Huadong Hospital, Fudan University, Shanghai, China.
Liu S; Department of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, China.
Gu W; Department of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, China.

Front Neurosci ; 15: 707944, 2021.

Article en En | MEDLINE | ID: mdl-34602967

Palabras clave

delayed neurocognitive recovery; functional connectivity; machine learning; resting-state functional magnetic resonance imaging; sparse representation

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Front Neurosci Año: 2021 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

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