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Functional Magnetic Resonance Imaging Data Analysis by Autoregressive Estimator

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Abstract

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.

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Acknowledgments

We thank S.D.R. Amaral and N. Caticha, for the simulated fMRI data set, and D. B. Araujo, for the real fMRI data set. This work received the support of FAPESP and CNPq. Script program can be made available by request to hernan@if.usp.br

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Authors and Affiliations

  1. Departamento de Física Geral, Instituto de Física, Universidade de São Paulo, Rua do Matão, Travessa R 187, Caixa Postal 66318, São Paulo, SP, CEP 05315-970, Brazil

    Hernán Cervantes & Said R. Rabbani

  2. Universidade Tecnológica Federal do Paraná, Medianeira, PR, Brazil

    Carlos A. C. Jousseph

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  1. Hernán Cervantes
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  2. Carlos A. C. Jousseph
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  3. Said R. Rabbani
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Correspondence to Hernán Cervantes.

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Cervantes, H., Jousseph, C.A.C. & Rabbani, S.R. Functional Magnetic Resonance Imaging Data Analysis by Autoregressive Estimator. Appl Magn Reson 43, 321–330 (2012). https://doi.org/10.1007/s00723-012-0371-4

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