Detecting the Information of Functional Connectivity Networks in Normal Aging Using Deep Learning From a Big Data Perspective.

Wen, Xin; Dong, Li; Chen, Junjie; Xiang, Jie; Yang, Jie; Li, Hechun; Liu, Xiaobo; Luo, Cheng; Yao, Dezhong

Wen, Xin; Dong, Li; Chen, Junjie; Xiang, Jie; Yang, Jie; Li, Hechun; Liu, Xiaobo; Luo, Cheng; Yao, Dezhong.

Afiliación

Wen X; College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
Dong L; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.
Chen J; School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Xiang J; College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
Yang J; College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
Li H; School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Liu X; School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Luo C; School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Yao D; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.

Front Neurosci ; 13: 1435, 2019.

Article en En | MEDLINE | ID: mdl-32009894

Palabras clave

DNN; aging; big data; deep learning; functional connectivity

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Front Neurosci Año: 2019 Tipo del documento: Article País de afiliación: China

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