Functional Magnetic Resonance Imaging Data Analysis by Autoregressive Estimator
Contribuinte(s) |
UNIVERSIDADE DE SÃO PAULO |
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Data(s) |
14/10/2013
14/10/2013
2012
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Resumo |
The autoregressive (AR) estimator, a non-parametric method, is used to analyze functional magnetic resonance imaging (fMRI) data. The same method has been used, with success, in several other time series data analysis. It uses exclusively the available experimental data points to estimate the most plausible power spectra compatible with the experimental data and there is no need to make any assumption about non-measured points. The time series, obtained from fMRI block paradigm data, is analyzed by the AR method to determine the brain active regions involved in the processing of a given stimulus. This method is considerably more reliable than the fast Fourier transform or the parametric methods. The time series corresponding to each image pixel is analyzed using the AR estimator and the corresponding poles are obtained. The pole distribution gives the shape of power spectra, and the pixels with poles at the stimulation frequency are considered as the active regions. The method was applied in simulated and real data, its superiority is shown by the receiver operating characteristic curves which were obtained using the simulated data. FAPESP FAPESP CNPq CNPq |
Identificador |
APPLIED MAGNETIC RESONANCE, WIEN, v. 43, n. 3, supl. 1, Part 1, pp. 321-330, OCT, 2012 0937-9347 http://www.producao.usp.br/handle/BDPI/34411 10.1007/s00723-012-0371-4 |
Idioma(s) |
eng |
Publicador |
SPRINGER WIEN WIEN |
Relação |
APPLIED MAGNETIC RESONANCE |
Direitos |
closedAccess Copyright SPRINGER WIEN |
Palavras-Chave | #SENSORY STIMULATION #FMRI #ACTIVATION #PHYSICS, ATOMIC, MOLECULAR & CHEMICAL #SPECTROSCOPY |
Tipo |
article original article publishedVersion |