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Tinnitus Abnormal Brain Region Detection Based on Dynamic Causal Modeling and Exponential Ranking

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 d...

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Detalles Bibliográficos
Autores principales: Tsai, Ming-Chuan, Cai, Yue-Xin, Wang, Chang-Dong, Zheng, Yi-Qing, Ou, Jia-Ling, Chen, Yan-Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6076911/
https://www.ncbi.nlm.nih.gov/pubmed/30105255
http://dx.doi.org/10.1155/2018/8656975
Descripción
Sumario: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.