A method to produce evolving functional connectivity maps during the course of an fMRI experiment using wavelet-based time-varying Granger causality.
Neuroimage
; 31(1): 187-96, 2006 May 15.
Article
em En
| MEDLINE
| ID: mdl-16434214
Functional magnetic resonance imaging (fMRI) is widely used to identify neural correlates of cognitive tasks. However, the analysis of functional connectivity is crucial to understanding neural dynamics. Although many studies of cerebral circuitry have revealed adaptative behavior, which can change during the course of the experiment, most of contemporary connectivity studies are based on correlational analysis or structural equations analysis, assuming a time-invariant connectivity structure. In this paper, a novel method of continuous time-varying connectivity analysis is proposed, based on the wavelet expansion of functions and vector autoregressive model (wavelet dynamic vector autoregressive-DVAR). The model also allows identification of the direction of information flow between brain areas, extending the Granger causality concept to locally stationary processes. Simulation results show a good performance of this approach even using short time intervals. The application of this new approach is illustrated with fMRI data from a simple AB motor task experiment.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Oxigênio
/
Processamento de Imagem Assistida por Computador
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Imageamento por Ressonância Magnética
/
Aumento da Imagem
/
Córtex Cerebral
/
Análise de Regressão
/
Modelos Estatísticos
/
Atividade Motora
/
Rede Nervosa
Tipo de estudo:
Diagnostic_studies
/
Etiology_studies
/
Risk_factors_studies
Limite:
Adult
/
Female
/
Humans
Idioma:
En
Revista:
Neuroimage
Assunto da revista:
DIAGNOSTICO POR IMAGEM
Ano de publicação:
2006
Tipo de documento:
Article
País de afiliação:
Brasil
País de publicação:
Estados Unidos