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Tinnitus Abnormal Brain Region Detection Based on Dynamic Causal Modeling and Exponential Ranking.
Tsai, Ming-Chuan; Cai, Yue-Xin; Wang, Chang-Dong; Zheng, Yi-Qing; Ou, Jia-Ling; Chen, Yan-Hong.
Afiliação
  • Tsai MC; School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China.
  • Cai YX; Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
  • Wang CD; Institute of Hearing and Speech-Language Science, Sun Yat-sen University, Guangzhou, China.
  • Zheng YQ; School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China.
  • Ou JL; Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
  • Chen YH; Institute of Hearing and Speech-Language Science, Sun Yat-sen University, Guangzhou, China.
Biomed Res Int ; 2018: 8656975, 2018.
Article em En | MEDLINE | ID: mdl-30105255
ABSTRACT
Dynamic Causal Modeling (DCM) has been extended for the analysis of electroencephalography (EEG) based on a specific biophysical and neurobiological generative model for EEG. Comparing to methods that summarize neural activities with linear relationships, the generative model enables DCM to better describe how signals are generated and better reveal the underlying mechanism of the activities occurring in human brains. Since DCM provides us with an approach to the effective connectivity between brain areas, with exponential ranking, the abnormality of the observed signals can be further located to a specific brain region. In this paper, a combination of DCM and exponential ranking is proposed as a new method aiming at searching for the abnormal brain regions which are associated with chronic tinnitus.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Zumbido / Encéfalo / Eletroencefalografia Tipo de estudo: Diagnostic_studies Limite: Female / Humans / Male Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Zumbido / Encéfalo / Eletroencefalografia Tipo de estudo: Diagnostic_studies Limite: Female / Humans / Male Idioma: En Ano de publicação: 2018 Tipo de documento: Article