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A dynamic system of brain networks revealed by fast transient EEG fluctuations and their fMRI correlates.
Hunyadi, B; Woolrich, M W; Quinn, A J; Vidaurre, D; De Vos, M.
Afiliação
  • Hunyadi B; STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU, Leuven, Belgium; imec, Leuven, Belgium. Electronic address: borbala.hunyadi@esat.kuleuven.be.
  • Woolrich MW; Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, United Kingdom.
  • Quinn AJ; Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, United Kingdom.
  • Vidaurre D; Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, United Kingdom.
  • De Vos M; Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
Neuroimage ; 185: 72-82, 2019 01 15.
Article em En | MEDLINE | ID: mdl-30287299
Resting state brain activity has become a significant area of investigation in human neuroimaging. An important approach for understanding the dynamics of neuronal activity in the resting state is to use complementary imaging modalities. Electrophysiological recordings can access fast temporal dynamics, while functional magnetic resonance imaging (fMRI) studies reveal detailed spatial patterns. However, the relationship between these two measures is not fully established. In this study, we used simultaneously recorded electroencephalography (EEG) and fMRI, along with Hidden Markov Modelling, to investigate how network dynamics at fast sub-second time-scales, accessible with EEG, link to the slower time-scales and higher spatial detail of fMRI. We found that the fMRI correlates of fast transient EEG dynamic networks show highly reproducible spatial patterns, and that their spatial organization exhibits strong similarity with traditional fMRI resting state networks maps. This further demonstrates the potential of electrophysiology as a tool for understanding the fast network dynamics that underlie fMRI resting state networks.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Encéfalo / Mapeamento Encefálico / Modelos Neurológicos / Rede Nervosa Tipo de estudo: Health_economic_evaluation Limite: Humans Idioma: En Revista: Neuroimage Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2019 Tipo de documento: Article País de publicação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Encéfalo / Mapeamento Encefálico / Modelos Neurológicos / Rede Nervosa Tipo de estudo: Health_economic_evaluation Limite: Humans Idioma: En Revista: Neuroimage Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2019 Tipo de documento: Article País de publicação: Estados Unidos