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Subcortical-cortical functional connectivity as a potential biomarker for identifying patients with functional dyspepsia.
Yin, Tao; Sun, Ruirui; He, Zhaoxuan; Chen, Yuan; Yin, Shuai; Liu, Xiaoyan; Lu, Jin; Ma, Peihong; Zhang, Tingting; Huang, Liuyang; Qu, Yuzhu; Suo, Xueling; Lei, Du; Gong, Qiyong; Liang, Fanrong; Li, Shenghong; Zeng, Fang.
Afiliação
  • Yin T; Acupuncture and Tuina School, The 3rd Teaching Hospital, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China.
  • Sun R; Acupuncture and Tuina School, The 3rd Teaching Hospital, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China.
  • He Z; Acupuncture and Tuina School, The 3rd Teaching Hospital, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China.
  • Chen Y; Key Laboratory of Sichuan Province for Acupuncture and Chronobiology, Chengdu, Sichuan 610075, China.
  • Yin S; International Education College, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China.
  • Liu X; First Affiliated Hospital, Henan University of Traditional Chinese Medicine, Zhengzhou, Henan 450002, China.
  • Lu J; Acupuncture and Tuina School, The 3rd Teaching Hospital, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China.
  • Ma P; Acupuncture and Tuina School, The 3rd Teaching Hospital, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China.
  • Zhang T; Acupuncture and Tuina School, The 3rd Teaching Hospital, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China.
  • Huang L; School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing 100029, China.
  • Qu Y; Acupuncture and Tuina School, The 3rd Teaching Hospital, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China.
  • Suo X; Acupuncture and Tuina School, The 3rd Teaching Hospital, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China.
  • Lei D; Acupuncture and Tuina School, The 3rd Teaching Hospital, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China.
  • Gong Q; Departments of Radiology, Huaxi Magnetic Resonance Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
  • Liang F; Departments of Radiology, Huaxi Magnetic Resonance Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
  • Li S; Departments of Radiology, Huaxi Magnetic Resonance Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
  • Zeng F; Acupuncture and Tuina School, The 3rd Teaching Hospital, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China.
Cereb Cortex ; 32(15): 3347-3358, 2022 07 21.
Article em En | MEDLINE | ID: mdl-34891153
The diagnosis of functional dyspepsia (FD) presently relies on the self-reported symptoms. This study aimed to determine the potential of functional brain network features as biomarkers for the identification of FD patients. Firstly, the functional brain Magnetic Resonance Imaging data were collected from 100 FD patients and 100 healthy subjects, and the functional brain network features were extracted by the independent component analysis. Then, a support vector machine classifier was established based on these functional brain network features to discriminate FD patients from healthy subjects. Features that contributed substantially to the classification were finally identified as the classifying features. The results demonstrated that the classifier performed pretty well in discriminating FD patients. Namely, the accuracy of classification was 0.84 ± 0.03 in cross-validation set and 0.80 ± 0.07 in independent test set, respectively. A total of 15 connections between the subcortical nucleus (the thalamus and caudate) and sensorimotor cortex, parahippocampus, orbitofrontal cortex were finally determined as the classifying features. Furthermore, the results of cross-brain atlas validation showed that these classifying features were quite robust in the identification of FD patients. In summary, the current findings suggested the potential of using machine learning method and functional brain network biomarkers to identify FD patients.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Mapeamento Encefálico / Dispepsia Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Mapeamento Encefálico / Dispepsia Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article