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Estimation of neuronal responses from fMRI data
M. Havlicek, J. Jan, M. Brazdil, VD. Calhoun,
Jazyk angličtina Země Spojené státy americké
Typ dokumentu časopisecké články, Research Support, N.I.H., Extramural, práce podpořená grantem
- MeSH
- algoritmy MeSH
- lidé MeSH
- magnetická rezonanční tomografie metody MeSH
- metoda Monte Carlo MeSH
- modely neurologické MeSH
- neurony fyziologie MeSH
- počítačová simulace MeSH
- poměr signál - šum MeSH
- statistika jako téma MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
In this paper we describe a deconvolution technique for estimation of the neuronal signal from an observed hemodynamic responses in fMRI data. Our approach, based on the Rauch-Tung-Striebel smoother for square-root cubature Kalman filter, enables us to accurately infer the hidden states, parameters, and the input of the dynamic system. Additionally, we enhance the cubature Kalman filter with a variational Bayesian approach for adaptive estimation of the measurement noise covariance.
Citace poskytuje Crossref.org
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- $a In this paper we describe a deconvolution technique for estimation of the neuronal signal from an observed hemodynamic responses in fMRI data. Our approach, based on the Rauch-Tung-Striebel smoother for square-root cubature Kalman filter, enables us to accurately infer the hidden states, parameters, and the input of the dynamic system. Additionally, we enhance the cubature Kalman filter with a variational Bayesian approach for adaptive estimation of the measurement noise covariance.
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